Dissertations / Theses on the topic 'Structural causal models'
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1
Oberst, Michael Karl. "Counterfactual policy introspection using structural causal models." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/124128.
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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 97-102).
Inspired by a growing interest in applying reinforcement learning (RL) to healthcare, we introduce a procedure for performing qualitative introspection and `debugging' of models and policies. In particular, we make use of counterfactual trajectories, which describe the implicit belief (of a model) of 'what would have happened' if a policy had been applied. These serve to decompose model-based estimates of reward into specific claims about specific trajectories, a useful tool for 'debugging' of models and policies, especially when side information is available for domain experts to review alongside the counterfactual claims. More specically, we give a general procedure (using structural causal models) to generate counterfactuals based on an existing model of the environment, including common models used in model-based RL. We apply our procedure to a pair of synthetic applications to build intuition, and conclude with an application on real healthcare data, introspecting a policy for sepsis management learned in the recently published work of Komorowski et al. (2018).
by Michael Karl Oberst.
S.M.
S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
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Odondi, Lang'O. "Causal modelling of survival data with informative noncompliance." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/causal-modelling-of-survival-data-with-informative-noncompliance(74f40dc0-e5d1-46c0-ab2f-ac42a3425ac7).html.
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Noncompliance to treatment allocation is likely to complicate estimation of causal effects in clinical trials. The ubiquitous nonrandom phenomenon of noncompliance renders per-protocol and as- treated analyses or even simple regression adjustments for noncompliance inadequate for causal inference. For survival data, several specialist methods have been developed when noncompliance is related to risk. The Causal Accelerated Life Model (CALM) allows time-dependent departures from randomized treatment in either arm and relates each observed event time to a potential event time that would have been observed if the control treatment had been given throughout the trial. Alternatively, the structural Proportional Hazards (C-Prophet) model accounts for all-or-nothing noncompliance in the treatment arm only while the CHARM estimator allows time-dependent departures from randomized treatment by considering survival outcome as a sequence of binary outcomes to provide an 'approximate' overall hazard ratio estimate which is adjusted for compliance. The problem of efficacy estimation is compounded for two-active treatment trials (additional noncompliance) where the ITT estimate provides a biased estimator for the true hazard ratio even under homogeneous treatment effects assumption. Using plausible arm-specific predictors of compliance, principal stratification methods can be applied to obtain principal effects for each stratum. The present work applies the above methods to data from the Esprit trials study which was conducted to ascertain whether or not unopposed oestrogen (hormone replacement therapy - HRT) reduced the risk of further cardiac events in postmenopausal women who survive a first myocardial infarction. We use statistically designed simulation studies to evaluate the performance of these methods in terms of bias and 95% confidence interval coverage. We also apply a principal stratification method to adjust for noncompliance in two treatment arms trial originally developed for binary data for survival analysis in terms of causal risk ratio. In a Bayesian framework, we apply the method to Esprit data to account for noncompliance in both treatment arms and estimate principal effects. We apply statistically designed simulation studies to evaluate the performance of the method in terms of bias in the causal effect estimates for each stratum. ITT analysis of the Esprit data showed the effects of taking HRT tablets was not statistically significantly different from placebo for both all cause mortality and myocardial reinfarction outcomes. Average compliance rate for HRT treatment was 43% and compliance rate decreased as the study progressed. CHARM and C-Prophet methods produced similar results but CALM performed best for Esprit: suggesting HRT would reduce risk of death by 50%. Simulation studies comparing the methods suggested that while both C-Prophet and CHARM methods performed equally well in terms of bias, the CALM method performed best in terms of both bias and 95% confidence interval coverage albeit with the largest RMSE. The principal stratification method failed for the Esprit study possibly due to the strong distribution assumption implicit in the method and lack of adequate compliance information in the data which produced large 95% credible intervals for the principal effect estimates. For moderate value of sensitivity parameter, principal stratification results suggested compliance with HRT tablets relative to placebo would reduce risk of mortality by 43% among the most compliant. Simulation studies on performance of this method showed narrower corresponding mean 95% credible intervals corresponding to the the causal risk ratio estimates for this subgroup compared to other strata. However, the results were sensitive to the unknown sensitivity parameter.
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Aten, Jason Erik. "Causal not confounded gene networks inferring acyclic and non-acyclic gene bayesian networks in mRNA expression studies using recursive v-structures, genetic variation, and orthogonal causal anchor structural equation models /." Diss., Restricted to subscribing institutions, 2007. http://proquest.umi.com/pqdweb?did=1563274791&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.
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Ewings, F. M. "Practical and theoretical considerations of the application of marginal structural models to estimate causal effects of treatment in HIV infection." Thesis, University College London (University of London), 2012. http://discovery.ucl.ac.uk/1346448/.
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Standard marginal structural models (MSMs) are commonly applied to estimate causal effects in the presence of time-dependent confounding; these may be extended to history-adjusted MSMs to estimate effects conditional on time-updated covariates, and dynamic MSMs to estimate e¤ects of pre-speci
ed dynamic regimes (Cain et al., 2010). We address methods to assess the optimal time for treatment initiation with respect to CD4 count in HIV-infected persons, and apply these to CASCADE cohort data. We advocate the application of all three types of MSM to address such causal questions and investigate gaps in the literature concerning their application. Of importance is the construction of suitable inverse probability weights. We have structured this process as four key decisions, de
fining a range of strategies; all demonstrated a bene
ficial effect of ART in CASCADE. We found a trend towards greater treatment bene
fit at lower CD4 across a range of models. Via large simulated randomised trials based on CASCADE data, longer grace periods (permitted delay in treatment initiation) and in particular less-frequently observed CD4 indicated higher optimal regimes (earlier treatment initiation at higher CD4), although similar AIDS-free survival rates may be achieved at these higher optimal regimes. In realistically-sized observational simulations, the optimal regime estimates lacked precision, mainly due to broadly constant AIDS-free survival rates at higher CD4. Optimal regimes estimated from dynamic MSMs should be interpreted with regard to the shape of the outcome-by-regime curve and the precision. In our clinical setting, we found that allowing a 3-month grace period may increase precision with little bias under the interpretation of no grace period; under longer grace periods, the bias outweighed the efficiency gain. In our CASCADE population, immediate treatment was preferable to delay, although estimation was limited by relatively short follow-up. Comparison across the MSM approaches offers additional insights into the methodology and clinical results.
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Rosich, Oliva Albert. "Sensor placement for fault diagnosis based on structural models: application to a fuel cell stak system." Doctoral thesis, Universitat Politècnica de Catalunya, 2011. http://hdl.handle.net/10803/53635.
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The present work aims to increase the diagnosis systems capabilities by choosing the location of sensors in the process. Therefore, appropriate sensor location will lead to better diagnosis performance and implementation easiness. The work is based on structural models ands some simplifications are considered in order to only focus on the sensor placement analysis. Several approaches are studied to solve the sensor placement problem. All of them find the optimal sensor configuration.
The sensor placement techniques are applied to a fuel cell stack system. The model used to describe the behaviour of this system consists of non-linear equations. Furthermore, there are 30 candidate sensors to improve the diagnosis specifications. The results obtained from this case study are used to strength the applicability of the proposed approaches.El present treball té per objectiu incrementar les prestacions dels diagnosticadors mitjançant la localització de sensors en el procés. D'aquesta manera, instal·lant els sensors apropiats s'obtenen millors diagnosticador i més facilitats d'implementació. El treball està basat en models estructurals i contempla una sèrie de simplificacions per tal de entrar-se només en la problemàtica de la localització de sensors. S'utilitzen diversos enfocs per tal de resoldre la localització de sensors, tot ells tenen com objectiu trobar la configuració òptima de sensors. Les tècniques de localització de sensors són aplicades a un sistema basat en una pila de combustible. El model d'aquest sistema està format per equacions no lineals. A més, hi ha la possibilitat d'instal·lar fins a 30 sensors per tal de millorar la diagnosis del sistema. Degut a aquestes característiques del sistema i del model, els resultats obtinguts mitjançant aquest cas d'estudi reafirmen l'aplicabilitat dels mètodes proposats.
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Dubois, Florent. "Dynamic models of segregation." Thesis, Aix-Marseille, 2017. http://www.theses.fr/2017AIXM0313.
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Cette thèse étudie les causes et conséquences du processus de ségrégation résidentielle dans l’Afrique du Sud (AFS) post-Apartheid. Nous nous intéressons à plusieurs aspects encore débattus dans la littérature. Le premier concerne l’impact des préférences des individus pour la composition raciale de leur voisinage sur la ségrégation. Le second a trait à l’impact de la ségrégation résidentielle sur les niveaux de revenus des différents groupes raciaux. Le dernier quantifie les différentes causes de la ségrégation. Dans le premier chapitre, nous réconcilions la littérature théorique sur l’impact des préférences pour la composition raciale du voisinage avec les observations empiriques de niveaux décroissants de ségrégation aux US et en AFS. Nous soutenons l’idée que si les individus internalisent les apports économiques et sociaux de chaque nouvel arrivant dans leur voisinage alors des voisinages intégrés peuvent émerger. Cet effet est empiriquement plus fort que l’homophilie et le racisme. Dans le second chapitre, nous étudions l’impact de la ségrégation sur l’ensemble de la distribution des revenus. Nous montrons que la ségrégation a un effet positif sur les hauts revenus pour les Blancs tandis qu’elle a un effet négatif pour les Noirs au bas de la distribution. L’effet de la ségrégation est souvent plus important que l’effet de l’éducation. Enfin, dans le troisième chapitre, nous quantifions l’impact de chaque déterminant de la ségrégation. Nous trouvons que le manque d’accès aux services publics de base est le déterminant principal, alors que les différences de caractéristiques sociodémographiques ne comptent que pour une faible part pour les quartiers les plus ségréguésThis thesis studies the causes and consequences of the residential segregation process in the post-Apartheid South Africa.Inside this general issue, we are interested in several aspects still debated in the literature on residential segregation. Thefirst concerns the impact of individuals’ preferences for the racial composition of their neighborhood on the segregationlevels. The second question deals with the impact of residential segregation on the income levels of each racial group. Thelast issue is related to quantifying the different causes of segregation.Three chapters constitute this thesis. In the first chapter, we reconcile the theoretical literature on the impact of preferencesfor the racial composition of the neighborhood with the empirical evidences of declining levels of segregation in theUnited-States and South Africa. We argue that if individuals internalize the economic and social life that a new entrantbrings with him, then integrated neighborhoods can emerge. This effect is empirically stronger than homophilly andracism. In the second chapter, we study the impact of residential segregation on the whole income distribution. We showthat residential segregation has a positif effect on top incomes for Whites, whereas it has a negatif effect for Blacks at thebottom of the distribution. The effect of residential segregation is even more important than the effect of education inmost cases. In the third chapter, we quantify the impact of each determinant of segregation. We find that the lackof access to basic public services is the main determinant, whereas differences in sociodemographics only account for asmall part in the most segregated areas
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Oba, Koji. "How to use marginal structural models in randomized trials to estimate the natural direct and indirect effects of therapies mediated by causal intermediates." 京都大学 (Kyoto University), 2011. http://hdl.handle.net/2433/152045.
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Bailly, Sébastien. "Utilisation des antifongiques chez le patient non neutropénique en réanimation." Thesis, Université Grenoble Alpes (ComUE), 2015. http://www.theses.fr/2015GREAS013/document.
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Les levures du genre Candida figurent parmi les pathogènes majeurs isolés chez les patients en soins intensifs et sont responsables d'infections systémiques : les candidoses invasives. Le retard et le manque de fiabilité du diagnostic sont susceptibles d'aggraver l'état du patient et d'augmenter le risque de décès à court terme. Pour respecter les objectifs de traitement, les experts recommandent de traiter le plus précocement possible les patients à haut risque de candidose invasive. Cette attitude permet de proposer un traitement précoce aux malades atteints, mais peut entraîner un traitement inutile et coûteux et favoriser l'émergence de souches de moindre sensibilité aux antifongiques utilisés.Ce travail applique des méthodes statistiques modernes à des données observationnelles longitudinales. Il étudie l'impact des traitements antifongiques systémiques sur la répartition des quatre principales espèces de Candida dans les différents prélèvements de patients en réanimation médicale, sur leur sensibilité à ces antifongiques, sur le diagnostic des candidémies ainsi que sur le pronostic des patients. Les analyses de séries de données temporelles à l'aide de modèles ARIMA (moyenne mobile autorégressive intégrée) ont confirmé l'impact négatif de l'utilisation des antifongiques sur la sensibilité des principales espèces de Candida ainsi que la modification de leur répartition sur une période de dix ans. L'utilisation de modèles hiérarchiques sur données répétées a montré que le traitement influence négativement la détection des levures et augmente le délai de positivité des hémocultures dans le diagnostic des candidémies. Enfin, l'utilisation des méthodes d'inférence causale a montré qu'un traitement antifongique préventif n'a pas d'impact sur le pronostic des patients non neutropéniques, non transplantés et qu'il est possible de commencer une désescalade précoce du traitement antifongique entre le premier et le cinquième jour après son initiation sans aggraver le pronosticCandida species are among the main pathogens isolated from patients in intensive care units (ICUs) and are responsible for a serious systemic infection: invasive candidiasis. A late and unreliable diagnosis of invasive candidiasis aggravates the patient's status and increases the risk of short-term death. The current guidelines recommend an early treatment of patients with high risks of invasive candidiasis, even in absence of documented fungal infection. However, increased antifungal drug consumption is correlated with increased costs and the emergence of drug resistance whereas there is yet no consensus about the benefits of the probabilistic antifungal treatment.The present work used modern statistical methods on longitudinal observational data. It investigated the impact of systemic antifungal treatment (SAT) on the distribution of the four Candida species most frequently isolated from ICU patients', their susceptibilities to SATs, the diagnosis of candidemia, and the prognosis of ICU patients. The use of autoregressive integrated moving average (ARIMA) models for time series confirmed the negative impact of SAT use on the susceptibilities of the four Candida species and on their relative distribution over a ten-year period. Hierarchical models for repeated measures showed that SAT has a negative impact on the diagnosis of candidemia: it decreases the rate of positive blood cultures and increases the time to positivity of these cultures. Finally, the use of causal inference models showed that early SAT has no impact on non-neutropenic, non-transplanted patient prognosis and that SAT de-escalation within 5 days after its initiation in critically ill patients is safe and does not influence the prognosis
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Bergman, Ruth. "Learning models of environments with manifest causal structure." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/36559.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.Includes bibliographical references (leaves 188-192).
by Ruth Bergman.
Ph.D.
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Baltar, Valéria Troncoso. "Equações estruturais aplicadas a modelos causais de câncer de pulmão." Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/6/6132/tde-01032011-150337/.
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Introdução: O câncer de pulmão (CP) é o tipo de câncer que mais mata no mundo e o cigarro ainda é sua causa mais importante. Além disso, a alimentação tem sido associada ao CP, por ser fonte de vitaminas e aminoácidos que fazem parte do metabolismo do carbono (MC). O MC é considerado mecanismo chave na manutenção da integridade do DNA e na regulação da expressão gênica, que, dessa forma, deve estar relacionado à carcinogênese. A ativação da imunidade está associada ao envelhecimento em indivíduos saudáveis, assim como a uma série de patologias, incluindo o câncer. Objetivo: Estudar como o MC, a ativação da imunidade e o tabaco estão relacionados ao risco de CP em um estudo caso-controle aninhado à coorte do EPIC (European Prospective Investigation into Cancer and Nutrition). Métodos: Para avaliar se os níveis plasmáticos de cotinina são um bom biomarcador da exposição ao tabaco, foram utilizados modelos lineares generalizados. Para avaliar os efeitos do tabaco, do MC e da ativação da imunidade no risco de CP, foram aplicados modelos de equações estruturais (MEE) de duas maneiras diferentes (com e sem variáveis latentes). Resultados: Com base nas respostas aos questionários de qualidade de vida, com relação às questões sobre fumo ativo e passivo, a cotinina se mostrou um bom biomarcador de exposição recente ao tabaco (tanto o aumento da exposição passiva quanto ativa foram significativas, P<0,001 e P<0,001 respectivamente). Em um MEE com variáveis observadas, incluindo o MC e a via de ativação da imunidade, a metionina e o folato como causas proximais apresentaram uma forte e inversa associação com o risco de CP. O aumento em um desvio-padrão nos níveis séricos de metionina e de folato significou uma redução no risco de 19 por cento (P<0,01) e 12 por cento (P=0,03) respectivamente. Em um MEE com variáveis latentes (cada uma representando o conjunto de vitaminas e aminoácidos importantes para promover: metilação de DNA, síntese de núcletídeos e imune ativação), foram encontrados efeitos protetores diretos da metilação do DNA (P=0,018) e da imune ativação (P=0,037); por outro lado, a síntese de nucletídeos não apresentou efeito no risco do câncer (P=0,098). Nas duas abordagens de MEE o cigarro permaneceu como a causa de maior impacto. Conclusões: A cotinina mostrou-se um bom biomarcador da exposição ao tabaco (ativa e passivamente). Confirmou-se que a via de metilação é um fator de proteção contra o CP. A ativação da imunidade apresentou um efeito direto de proteção contra o CP no modelo com variáveis latentes, equanto que, a síntese de nucletídeos não apresentou relação com o CP. O tabaco continua sendo o fator de maior impacto no risco de CPBackground: Lung cancer (LC) continues to be the most common cancer death in the world. Tobacco exposure continues to be the most important cause. In addition, micronutrient intake has been linked to LC, because they are the main source of vitamins and amino acids involved in the one-carbon metabolism (OCM) which is considered key in maintaining DNA integrity, regulating gene expression, and may thus affect carcinogenesis. Immune activation is involved in the aging process in normal healthy individuals as well as in a number of pathologies, including cancer. Objectives: To investigate how OCM, immune activation and tobacco are related to LC incidence in a nested case-control study from the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Methods: To validate plasma cotinine levels as a good biomarker for tobacco exposure, a generalized linear model was applied. To evaluate the effects of tobacco, OCM and immune activation in LC, structural equation models (SEM) were applied in two different ways. Results: Based on questions about smoking, passive smoking and number of cigarettes smoked, it was shown that cotinine is a good biomarker for tobacco exposure (passive and active exposure with significant relation, p<0.001 and P<0.001, respectively). In a SEM model with only observed variables, including OCM and immune activation, methionine and folate as proximal causes presented a strong and inverse relation with LC risk. An increase in one standard deviation of serum levels of methionine and folate meant a 19 per cent (P<0.01) and 12 per cent (P<0.01) reduction in LC risk, respectively. In a SEM including latent variables (each one including vitamins and amino acids important to promote DNA methylation, nucleotide synthesis and immune activity), a direct and protective effect for DNA methylation (p=0.018) and immune activation was found (p=0.037), whereas nucleotide synthesis did not present a significant total effect. In both approaches of SEM, tobacco exposure remains with the highest impact on LC risk. Conclusions: It was found that cotinine is a good biomarker of tobacco exposure (active and passive). It was confirmed that methylation protects against LC. Immune activation presented a direct protective effect in the latent model, while nucleotide synthesis was not confirmed to be related to LC risk. Tobacco effect remains as the factor with highest impact in lung cancer
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Suh, Youngkyoon. "Exploring Causal Factors of DBMS Thrashing." Diss., The University of Arizona, 2015. http://hdl.handle.net/10150/556213.
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Modern DBMSes are designed to support many transactions running simultaneously. DBMS thrashing is indicated by the existence of a sharp drop in transaction throughput. The thrashing behavior in DBMSes is a serious concern to DBAs engaged in on-line transaction processing (OLTP) and on-line analytical processing (OLAP) systems, as well as to DBMS implementors developing technologies related to concurrency control. If thrashing is prevalent in a DBMS, thousands of transactions may be aborted, resulting in little progress in transaction throughput over time. From an engineering perspective, therefore, it is of critical importance to understand the factors of DBMS thrashing. However, understanding the origin of modern DBMSes' thrashing is challenging, due to many factors that may interact. The existing literature on thrashing exhibits the following weaknesses: (i) methodologies have been based on simulation and analytical studies, rather than on empirical analysis on real DBMSes, (ii) scant attention has been paid to the associations between factors, and (iii) studies have been restricted to one specific DBMS rather than across multiple DBMSes. This dissertation aims at better understanding the thrashing phenomenon across multiple DBMSes. We identify the underlying causes and propose a novel structural causal model to explicate the relationships between various factors contributing to DBMS thrashing. Our model derives a number of specific hypotheses to be subsequently tested across DBMSes, providing empirical support for this model as well as engineering implications for fundamental improvements in transaction processing. Our model also guides database researchers to refine this causal model, by looking into other unknown factors.
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Hütter, Jan-Christian Klaus. "Minimax estimation with structured data : shape constraints, causal models, and optimal transport." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122184.
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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 2019Cataloged from PDF version of thesis.
Includes bibliographical references (pages 275-299).
Modern statistics often deals with high-dimensional problems that suffer from poor performance guarantees and from the curse of dimensionality. In this thesis, we study how structural assumptions can be used to overcome these difficulties in several estimation problems, spanning three different areas of statistics: shape-constrained estimation, causal discovery, and optimal transport. In the area of shape-constrained estimation, we study the estimation of matrices, first under the assumption of bounded total-variation (TV) and second under the assumption that the underlying matrix is Monge, or supermodular. While the first problem has a long history in image denoising, the latter structure has so far been mainly investigated in the context of computer science and optimization. For TV denoising, we provide fast rates that are adaptive to the underlying edge sparsity of the image, as well as generalizations to other graph structures, including higher-dimensional grid-graphs. For the estimation of Monge matrices, we give near minimax rates for their estimation, including the case where latent permutations act on the rows and columns of the matrix. In the latter case, we also give two computationally efficient and consistent estimators. Moreover, we show how to obtain estimation rates in the related problem of estimating continuous totally positive distributions in 2D. In the area of causal discovery, we investigate a linear cyclic causal model and give an estimator that is near minimax optimal for causal graphs of bounded in-degree. In the area of optimal transport, we introduce the notion of the transport rank of a coupling and provide empirical and theoretical evidence that it can be used to significantly improve rates of estimation of Wasserstein distances and optimal transport plans. Finally, we give near minimax optimal rates for the estimation of smooth optimal transport maps based on a wavelet regularization of the semi-dual objective.
by Jan-Christian Klaus Hütter.
Ph. D.
Ph.D. Massachusetts Institute of Technology, Department of Mathematics
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Agrawal, Raj S. M. Massachusetts Institute of Technology. "Minimal I-MAP MCMC for scalable structure discovery in causal DAG models." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/128412.
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Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2020Cataloged from PDF version of thesis. "February 2020."
Includes bibliographical references (pages 49-51).
Learning a Bayesian network (BN) from data can be useful for decision-making or discovering causal relationships. However, traditional methods often fail in modern applications, which exhibit a larger number of observed variables than data points. The resulting uncertainty about the underlying network as well as the desire to incorporate prior information recommend a Bayesian approach to learning the BN, but the highly combinatorial structure of BNs poses a striking challenge for inference. The current state-of-the-art methods such as order MCMC are faster than previous methods but prevent the use of many natural structural priors and still have running time exponential in the maximum indegree of the true directed acyclic graph (DAG) of the BN. We here propose an alternative posterior approximation based on the observation that, if we incorporate empirical conditional independence tests, we can focus on a high-probability DAG associated with each order of the vertices. We show that our method allows the desired flexibility in prior specification, removes timing dependence on the maximum indegree, and yields provably good posterior approximations; in addition, we show that it achieves superior accuracy, scalability, and sampler mixing on several datasets.
by Raj Agrawal.
S.M.
S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
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Triplett, Josh. "Relativistic Causal Ordering A Memory Model for Scalable Concurrent Data Structures." PDXScholar, 2012. https://pdxscholar.library.pdx.edu/open_access_etds/497.
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High-performance programs and systems require concurrency to take full advantage of available hardware. However, the available concurrent programming models force a difficult choice, between simple models such as mutual exclusion that produce little to no concurrency, or complex models such as Read-Copy Update that can scale to all available resources. Simple concurrent programming models enforce atomicity and causality, and this enforcement limits concurrency. Scalable concurrent programming models expose the weakly ordered hardware memory model, requiring careful and explicit enforcement of causality to preserve correctness, as demonstrated in this dissertation through the manual construction of a scalable hash-table item-move algorithm. Recent research on "relativistic programming" aims to standardize the programming model of Read-Copy Update, but thus far these efforts have lacked a generalized memory ordering model, requiring data-structure-specific reasoning to preserve causality. I propose a new memory ordering model, "relativistic causal ordering", which combines the scalabilty of relativistic programming and Read-Copy Update with the simplicity of reader atomicity and automatic enforcement of causality. Programs written for the relativistic model translate to scalable concurrent programs for weakly-ordered hardware via a mechanical process of inserting barrier operations according to well-defined rules. To demonstrate the relativistic causal ordering model, I walk through the straightforward construction of a novel concurrent hash-table resize algorithm, including the translation of this algorithm from the relativistic model to a hardware memory model, and show through benchmarks that the resulting algorithm scales far better than those based on mutual exclusion.
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Boles, Myra. "A Causal Model of Hospital Volume, Structure and Process Indicators, and Surgical Outcomes." VCU Scholars Compass, 1994. https://scholarscompass.vcu.edu/etd/4370.
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This research developed and tested a conceptual model to explain why higher volumes of certain surgical procedures lead to better patient outcomes. The model incorporated hospital structural characteristics and process of care indicators to explain the volume-outcome relationship. The volume-outcome relationship was further examined longitudinally to determine stability over time and to substantiate the causality implied by the conceptual model.
A sample (n=1752) of acute-care, general hospitals was selected from hospitals that performed, in 1990, at least one surgical procedure on Medicare patients of the following: reduction of hip fracture, cholecystectomy, hip replacement, carotid endarterectomy, and pacemaker insertion. For the longitudinal analysis, the sample size was reduced to 1582 hospitals that performed all five surgical procedures in 1988 and in 1990. The conceptual model was specified as a structural equation model, and was analyzed using LISREL 7. The cross-sectional analysis examined interrelationships among volume, resource availability, average length of stay, structure, process, and outcome. Panel data were used to examine the stability of volume and outcome from 1988 to 1990.
The hypothesized volume-outcome relationship existed for hip fracture and cholecystectomy, and the effects of structure and process on outcome were significant for hip fracture and hip replacement. No volume effects were detected for hip replacement, carotid endarterectomy, and pacemaker insertion. In all cases, volume, average length of stay, and resource availability had significant influence on the hospital's structure and process of care. Panel data were relatively stable for volume, but unstable for outcome.
The volume-outcome relationship is procedure-specific. For hip fracture and cholecystectomy, the direct effect of volume on outcome is small after taking into account structure and process. The indirect effect of volume leads to inefficient care processes and attenuates the beneficial, direct effects of high volume.
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Aka, Niels Mariano [Verfasser]. "Three Essays on Model Selection in Time Series Econometrics : Model Averaging, Causal Graphs, and Structural Identification / Niels Mariano Aka." Berlin : Freie Universität Berlin, 2021. http://d-nb.info/1229436685/34.
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Kim, Seehyung. "A Causal Model of Linkages between Environment and Organizational Structure, and Its Performance Implications in International Service Distribution: An Empirical Study of Restaurant and Hotel Industry." Diss., Virginia Tech, 2005. http://hdl.handle.net/10919/27373.
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This research develops and tests a model of the service unit ownership and control patterns used by international service companies. The main purpose of this study is to investigate trivariate causal relationships among environmental factors, organizational structure, and perceived performance in the internationalization process of service firms. A service firm operating in foreign soil has a choice of three general entry mode strategies offering different degrees of ownership and control of its remote operating units located in foreign countries -- full ownership arrangement, joint venture arrangement, and franchising arrangement.
The entry mode strategies chosen depend on the factors relating to internal environment of a specific firm, industry related factors in which the firm operates, and external environment of the operating units at national context. This study identifies these factors, investigates how they affect the firm's choice of entry modes, and finally examines the impact of entry mode on firm's performance. The overall model has been explained by contingency theory that conceptualizes optimal level of ownership and control mode as a response by the firm to the interplay of environmental factors and as a determinant of firm's performance. To this core can be added complementary theories which are borrowed from agency theory, transaction cost theory, and resource dependence theory. These theories explain the linkages between market entry mode and each type of environmental factors.
In order to empirically test the hypotheses, data were collected from hospitality firms regarding the ownership structure of subsidiaries located in foreign countries. As a whole, the conceptual model developed in the study received strong support from the empirical study. This study found a positive impact of contingency fit on performance and so support contingency theory in which some combinations of the environmental dimensions and organizational structure will lead to better organizational performance. Another finding of this study indicates that the increased level of ownership and control will result in enhancing the level of perceived performance. It should be noted that contingency model-based mode choice would provide managers with the optimal performance because there is not one best performing mode choice in volatile international market.
Next, the relationship of market environment with organizational structure was examined through three different perspectives. Market environment was investigated at firm, industry, and national context, which includes five factors -- monitoring uncertainty, asset specificity, cultural distance, political uncertainty, and economic uncertainty. The model is suggestive of a picture in which five environmental factors vie for affecting the choice of market entry modes. All five environmental factors were found to be significantly related to firms' organizational structure. Among five environmental factors, cultural uncertainty has the largest effect on the choice of entry mode followed by monitoring uncertainty, political uncertainty, asset specificity, and economic uncertainty.
One of the important implications of this research is the inclusion of franchising as an actual management strategy and competitive business practice that is related to international ownership and control strategy. Higher degrees of uncertainty associated with the foreign market encourage external dependence of the venture, in which the operation depends more heavily on local relationships. Franchising substitutes the loss of ownership by an increase of external relationships and it takes without losing control on retail operation. Resource exploitation depends on the local market for either inputs or outputs for better performance. Understanding the fit between the each set of contingent variables and the elements of ownership and control strategy will allow marketers to determine when franchising is the suitable mode of operation in global markets.
Collectively, these results suggest that the choice of an organizational form for international service firms involves a complex balance of firm, industry, and country level factors. Managers can maximize performance by aligning entry mode strategy with external contextual circumstances as well as internal resources. Managers may also be able to make better mode choice decisions using the theory-driven criteria examined in this study, increasing their chances for financial and non-financial success.Ph. D.
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Busko, Deborah Ann. "Causes and consequences of perfectionism and procrastination, a structural equation model." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape15/PQDD_0004/MQ31814.pdf.
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Börsum, Jakob. "Estimating Causal Effects Of Relapse Treatment On The Risk For Acute Myocardial Infarction Among Patients With Diffuse Large B-Cell Lymphoma." Thesis, Uppsala universitet, Statistiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447241.
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This empirical register study intends to estimate average causal effects of relapse treatment on the risk for acute myocardial infarction (AMI) among patients with Diffuse B-Cell Lymphoma (DLBCL) within the potential outcome framework. The report includes a brief introduction to causal inference and survival anal- ysis and mentions specific causal parameters of interest that will be estimated. A cohort of 2887 Swedish DLBCL patients between 2007 and 2014 were included in the study where 560 patients suffered a relapse. The relapse treatment is hypothesised to be cardiotoxic and induces an increased risk of heart diseases. The identifiability assumptions need to hold to estimate average causal effects and are assessed in this report. The patient cohort is weighted using inverse probability of treatment and censoring weights and potential marginal survival curves are estimated from marginal structural Cox models. The resulting point estimate indicates a protective causal effect of relapse treatment on AMI but estimated bootstrap confidence intervals suggest no significant effect on the 5% significance level.
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Leung, Chi Ho. "Necessary and Sufficient Conditions on State Transformations That Preserve the Causal Structure of LTI Dynamical Networks." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7413.
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Linear time-invariant (LTI) dynamic networks are described by their dynamical structure function, and generally, they have many possible state space realizations. This work characterizes the necessary and sufficient conditions on a state transformation that preserves the dynamical structure function, thereby generating the entire set of realizations of a given order for a specific dynamic network.
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Chong, Hogun. "A causal model of linkages among strategy, structure, and performance using directed acyclic graphs: A manufacturing subset of Fortune 500 industrials 1990-1998." Texas A&M University, 2003. http://hdl.handle.net/1969.1/58.
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This research explored the causal relationships among strategies, corporate structure, and performance of the largest U.S. non-financial firms using Directed Acyclic Graphs (DAGs). Corporate strategies and structure have been analyzed as major variables to influence corporate performance in management and organizational studies. However, their causal relationships in terms of which variables are leaders and followers, as well as the choices of variables to configure them, are controversial. Finding of causal relationships among strategic variables, structural variables, and corporate performance is beneficial to researchers as well as corporate mangers. It provides guidance to researchers how to build a model in order to measure influences from one variable to the other, lowering the risk of drawing spurious conclusions. It also provides managers a prospect of how certain important variables would change by making a certain strategic decision. Literatures from agency theory, transactional cost economics, and traditional strategic management perspective are used to suggest variables essential to analyze corporate performance. This study includes size and multi-organizational ownership hierarchy as variables to configure corporate structure. The variables to configure corporate strategies are unrelated and related diversification, ownership by institutional investors, debt, investment in R&D, and investment in advertisement.
The study finds that most of the variables classified as corporate strategy and corporate structure variables are either direct or indirect causes of corporate accounting performance. Generally, results supports the relational model: corporate structure® corporate strategy® corporate performance. Ownership hierarchy structure, unrelated diversification, advertising expenses, and R&D intensity have direct causal influences on corporate accounting performance. Size and related diversification affected corporate accounting performance indirectly, both through ownership hierarchy structure. Theoretical causal relationships from agency theory are less supported than those from transaction cost economics and traditional strategic management perspective. Further my study suggests that, in general, good corporate performance in 1990s was mainly achieved by internal expansion through investment in R&D and advertisement, rather than external expansion of firms through unrelated diversification, related diversification, and expansion of ownership hierarchy.
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Brunelli, Renata Trevisan. "Análise do impacto de perturbações sobre medidas de qualidade de ajuste para modelos de equações estruturais." Universidade de São Paulo, 2012. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-24032013-123415/.
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A Modelagem de Equações Estruturais (SEM, do inglês Structural Equation Modeling) é uma metodologia multivariada que permite estudar relações de causa/efeito e correlação entre um conjunto de variáveis (podendo ser elas observadas ou latentes), simultaneamente. A técnica vem se difundindo cada vez mais nos últimos anos, em diferentes áreas do conhecimento. Uma de suas principais aplicações é na conrmação de modelos teóricos propostos pelo pesquisador (Análise Fatorial Conrmatória). Existem diversas medidas sugeridas pela literatura que servem para avaliar o quão bom está o ajuste de um modelo de SEM. Entretanto, é escassa a quantidade de trabalhos na literatura que listem relações entre os valores de diferentes medidas com possíveis problemas na amostra e na especicação do modelo, isto é, informações a respeito de que possíveis problemas desta natureza impactam quais medidas (e quais não), e de que maneira. Tal informação é importante porque permite entender os motivos pelos quais um modelo pode estar sendo considerado mal-ajustado. O objetivo deste trabalho é investigar como diferentes perturbações na amostragem, especicação e estimação de um modelo de SEM podem impactar as medidas de qualidade de ajuste; e, além disso, entender se o tamanho da amostra influencia esta resposta. Simultaneamente, também se avalia como tais perturbações afetam as estimativas, dado que há casos de perturbações em que os parâmetros continuam sendo bem ajustados, mesmo com algumas medidas indicando um mau ajuste; ao mesmo tempo, há ocasiões em que se indica um bom ajuste, enquanto que os parâmetros são estimados de forma distorcida. Tais investigações serão realizadas a partir de simulações de exemplos de amostras de diferentes tamanhos para cada tipo de perturbação. Então, diferentes especicações de modelos de SEM serão aplicados a estas amostras, e seus parâmetros serão estimados por dois métodos diferentes: Mínimos Quadrados Generalizados e Máxima Verossimilhança. Conhecendo tais resultados, um pesquisador que queira aplicar a técnica de SEM poderá se precaver e, dentre as medidas de qualidade de ajuste disponíveis, optar pelas que mais se adequem às características de seu estudo.The Structural Equation Modeling (SEM) is a multivariate methodology that allows the study of cause-and-efect relationships and correlation of a set of variables (that may be observed or latent ones), simultaneously. The technique has become more diuse in the last years, in different fields of knowledge. One of its main applications is on the confirmation of theoretical models proposed by the researcher (Confirmatory Factorial Analysis). There are several measures suggested by literature to measure the goodness of t of a SEM model. However, there is a scarce number of texts that list relationships between the values of different of those measures with possible problems that may occur on the sample or the specication of the SEM model, like information concerning what problems of this nature impact which measures (and which not), and how does the impact occur. This information is important because it allows the understanding of the reasons why a model could be considered bad fitted. The objective of this work is to investigate how different disturbances of the sample, the model specification and the estimation of a SEM model are able to impact the measures of goodness of fit; additionally, to understand if the sample size has influence over this impact. It will also be investigated if those disturbances affect the estimates of the parameters, given the fact that there are disturbances for which occurrence some of the measures indicate badness of fit but the parameters are not affected; at the same time, that are occasions on which the measures indicate a good fit and there are disturbances on the estimates of the parameters. Those investigations will be made simulating examples of different size samples for which type of disturbance. Then, SEM models with different specifications will be fitted to each sample, and their parameters will be estimated by two dierent methods: Generalized Least Squares and Maximum Likelihood. Given those answers, a researcher that wants to apply the SEM methodology to his work will be able to be more careful and, among the available measures of goodness of fit, to chose those that are more adequate to the characteristics of his study.
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Clarke, Richard. "An assessment of the causal attributions of care staff working with learning disabled people : the application of a formal structured model and qualitative measures." Thesis, Bangor University, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.318508.
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Plagnes, Valérie. "Structure et fonctionnement des aquifères karstiques : caractérisation par la géochimie des eaux." Montpellier 2, 1997. http://www.theses.fr/1997MON20193.
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Depuis 1991, le brgm a lance, en association avec le cnrs, un programme de recherche sur la connaissance de la structure et du fonctionnement des karsts en vue de leur exploitation. Dans un premier temps, une synthese des methodes d'evaluation de la ressource du karst d'un point de vue hydrodynamique a ete effectuee (these b. Marsaud, 1996). Dans le prolongement de ces travaux, une caracterisation hydrogeochimique des ecoulements souterrains d'origine karstique a ete realisee. 1. Dans un premier temps, une systhese des concepts et methodes de l'hydrogeochimie des eaux souterraines en milieu carbonate a ete realisee. Les informations apportees par les differents traceurs geochimiques ont ete identifiees ainsi que l'ensemble des methodes mises en oeuvre pour exploiter les donnees hydrogeochimiques et interpreter les variations du chimisme des eaux aux exutoires karstiques. 2. Dans un second temps, une etude a ete appliquee aux karsts du larzac. Elle a permis d'analyser les reponses geochimiques de plusieurs differents et d'en extraire les informations relatives au fonctionnement et a la structure de ces aquiferes. Ceux-ci ont fait l'objet d'un suivi hydrodynamique et hydrogeochimique pendant plusieurs cycles hydrologiques (2 a 5 cycles). Cette etude vise d'une part a ameliorer la connaissance de ces karsts d'un point de vue applique, mais surtout, cette analyse a pour objectif d'aborder certains aspects fondamentaux du fonctionnement des karsts. A travers l'exemple du larzac, ce sont les variations du chimisme dans les aquiferes karstiques en general que nous avons cherche a comprendre. 3. Afin d'obtenir une vision d'ensemble de l'hydrogeochimie en milieu karstique, les resultats obtenus sur le larzac ont ete compares a ceux issus d'autres systemes karstiques. Puis, une methodologie d'etude hydrogeochimique des karsts a ete proposee en fonction des donnees disponibles et des informations recherchees.
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Fagua, José Camilo. "Geospatial Modeling of Land Cover Change in the Chocó-Darien Global Ecoregion of South America: Assessing Proximate Causes and Underlying Drivers of Deforestation and Reforestation." DigitalCommons@USU, 2018. https://digitalcommons.usu.edu/etd/7362.
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The Chocó-Darien Global Ecoregion (CGE) in South America is one of 25 global biodiversity hotspots prioritized for conservation. I performed the first land-use and land-cover (LULC) change analysis for the entire CGE in this dissertation. There were three main objectives: 1) Select the best available imagery to build annual land-use and land-cover maps from 2001 to 2015 across the CGE. 2) Model LULC across the CGE to assess forest change trends from 2002 to 2015 and identify the effect of proximate causes of deforestation and reforestation. 3) Estimate the effects of underlying drivers on deforestation and reforestation across the CGE between 2002 and 2015. I developed annual LULC maps across the CGE from 2002 to 2015 using MODIS (Moderate Resolution Imaging Spectro radiometer) vegetation index products and random forest classification. The LULC maps resulted in high accuracies (Kappa = 0.87; SD = 0.008). We detected a gradual replacement of forested areas with agriculture and secondary vegetation (agriculture reverting to early regeneration of natural vegetation) across the CGE. Forest loss was higher between 2010-2015 when compared to 2002-2010. LULC change trends, proximate causes, and reforestation transitions varied according to administrative authority (countries: PanamanianCGE, Colombian CGE, and Ecuadorian CGE). Population growth and road density were underlying drivers of deforestation. Armed conflicts, Gross Domestic Product, and average annual rain were proximate causes and underlying drivers related reforestation.
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Islam, Md Tazul. "Unraveling the relationship between trip chaining and mode choice using Structural Equation Models." Master's thesis, 2010. http://hdl.handle.net/10048/1127.
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Trip chaining and mode choice are two important travel behavior decisions in activity-based travel demand modeling system. The hierarchy of these two decisions influences models predictive capability and policy sensitivity. This thesis is aimed at investigating the hierarchical relationship between these decisions and also the effects of socio-demographic characteristics on them. Structural Equation Modeling (SEM) technique is used for this investigation. A six week travel diary data collected in Thurgau, Switzerland in 2003 is used for model estimation. Model estimation results show that for work-tour, trip chain and mode choice decisions are simultaneous and it remains consistent across the six weeks. For weekdays non-work tour, mode choice precedes trip chain whereas for weekends non-work tour trip chain precedes mode choice. The investigation of the effect of a number of socio-demographic characteristics on trip chaining and mode choice behaviors is also found useful for better understanding of these behaviors.Transportation Engineering
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Hwang, Kyudae. "A structural approach to estimating sex-based wage discrimination causal and indicator models /." 1987. http://catalog.hathitrust.org/api/volumes/oclc/17314412.html.
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Thesis (Ph. D.)--University of Wisconsin--Madison, 1987.Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 104-115).
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Jiang, Tammy. "Suicide and non-fatal suicide attempts among persons with depression in the population of Denmark." Thesis, 2021. https://hdl.handle.net/2144/42580.
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Depression increases the risk of suicide death and non-fatal suicide attempt. Between 2 - 6% of persons with depression will die by suicide1 and 25 - 31% of persons with depression will make a non-fatal suicide attempt during their lifetime.2,3 Despite the strong association between depression and suicidal behavior, the vast majority of persons with depression will not engage in suicidal behavior, making it difficult to accurately predict who is at risk for suicide and non-fatal suicide attempt. Identifying high risk persons who should be connected to suicide prevention interventions is an important public health goal. Furthermore, depression often co-occurs with other mental disorders, which may exert an interactive influence on the risk of suicide and suicide attempt. Understanding the joint influence of depression and other mental disorders on suicide outcomes may inform prevention strategies. The goals of this dissertation were to predict suicide and non-fatal suicide attempt among persons with depression and to quantify the causal joint effect of depression and comorbid psychiatric disorders on suicide and suicide attempt. For all three studies, we used data from Danish registries, which routinely collect high-quality data in a setting of universal health care with long-term follow-up and registration of most health and life events.4
In Study 1, we predicted suicide deaths among men and women diagnosed with depression using a case-cohort design (n = 14,737). Approximately 800 predictors were included in the machine learning models (classification trees and random forests), spanning demographic characteristics, income, employment, immigrant status, citizenship, family suicidal history (parent or spouse), previous suicide attempts, mental disorders, physical health disorders, surgeries, prescription drugs, and psychotherapy. In depressed men, we found interactions between hypnotics and sedatives, analgesics and antipyretics, and previous poisonings that were associated with a high risk of suicide. In depressed women, there were interactions between poisoning and anxiolytics and between anxiolytics and hypnotics and sedatives that were associated with suicide risk. The variables in the random forests that contributed the most to prediction accuracy in depressed men were previous poisoning diagnoses and prescriptions of hypnotics and sedatives and anxiolytics. In depressed women, the most important predictors of suicide were receipt of state pension, prescriptions for psychiatric medications (anxiolytics and antipsychotics) and diagnoses of poisoning, alcohol related disorders, and reaction to severe stress and adjustment disorders. Prescriptions of analgesics and antipyretics (e.g., acetaminophen) and antithrombotic agents (e.g., aspirin) emerged as important predictors for both depressed men and women.
Study 2 predicted non-fatal suicide attempts among men and women diagnosed with depression using a case-cohort design (n = 17,995). Among depressed men, there was a high risk of suicide attempt among those who received a state pension and were diagnosed with toxic effects of substances. There was also an interaction between reaction to severe stress and adjustment disorder and not receiving psychological help that was associated with suicide attempt risk among depressed men. In depressed women, suicide attempt risk was high in those who were prescribed antipsychotics, diagnosed with specific personality disorders, did not have a poisoning diagnosis, and were not receiving a state pension. For both men and women, the random forest results showed that the strongest contributors to prediction accuracy of suicide attempts were poisonings, alcohol related disorders, reaction to severe stress and adjustment disorders, drugs used to treat psychiatric disorders (e.g., drugs used in addictive disorders, anxiolytics, hypnotics and sedatives), anti-inflammatory medications, receipt of state pension, and remaining single.
Study 3 examined the joint effect of depression and other mental disorders on suicide and non-fatal suicide attempts using a case-cohort design (suicide death analysis n = 279,286; suicide attempt analysis n = 288,157). We examined pairwise combinations of depression with: 1) organic disorders, 2) substance use disorders, 3) schizophrenia, 4) bipolar disorder, 5) neurotic disorders, 6) eating disorders, 7) personality disorders, 8) intellectual disabilities, 9) developmental disorders, and 10) behavioral disorders. We fit sex-stratified joint marginal structural Cox models to account for time-varying confounding. We observed large hazard ratios for the joint effect of depression and comorbid mental disorders on suicide and suicide attempts, the effect of depression in the absence of comorbid mental disorders, and for the effect of comorbid mental disorders in the absence of depression. We observed positive and negative interdependence between different combinations of depression and comorbid mental disorders on the rate of suicide and suicide attempt, with variation by sex. Overall, depression and comorbid mental disorders are harmful exposures, both independently and jointly.
All of the studies in this dissertation highlight the important role of interactions between risk factors in suicidal behavior among persons with depression. Depression is one of the most commonly assessed risk factors for suicide,5,6 and our findings underscore the value of considering additional risk factors such as other psychiatric disorders, psychiatric medications, and social factors in combination with depression. The results of this dissertation may help inform potential risk identification strategies which may facilitate the targeting of suicide prevention interventions to those most vulnerable.
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(5929691), Asish Ghoshal. "Efficient Algorithms for Learning Combinatorial Structures from Limited Data." Thesis, 2019.
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Recovering combinatorial structures from noisy observations is a recurrent problem in many application domains, including, but not limited to, natural language processing, computer vision, genetics, health care, and automation. For instance, dependency parsing in natural language processing entails recovering parse trees from sentences which are inherently ambiguous. From a computational standpoint, such problems are typically intractable and call for designing efficient approximation or randomized algorithms with provable guarantees. From a statistical standpoint, algorithms that recover the desired structure using an optimal number of samples are of paramount importance.
We tackle several such problems in this thesis and obtain computationally and statistically efficient procedures. We demonstrate optimality of our methods by proving fundamental lower bounds on the number of samples needed by any method for recovering the desired structures. Specifically, the thesis makes the following contributions:
(i) We develop polynomial-time algorithms for learning linear structural equation models --- which are a widely used class of models for performing causal inference --- that recover the correct directed acyclic graph structure under identifiability conditions that are weaker than existing conditions. We also show that the sample complexity of our method is information-theoretically optimal.
(ii) We develop polynomial-time algorithms for learning the underlying graphical game from observations of the behavior of self-interested agents. The key combinatorial problem here is to recover the Nash equilibria set of the true game from behavioral data. We obtain fundamental lower bounds on the number of samples required for learning games and show that our method is statistically optimal.
(iii) Lastly, departing from the generative model framework, we consider the problem of structured prediction where the goal is to learn predictors from data that predict complex structured objects directly from a given input. We develop efficient learning algorithms that learn structured predictors by approximating the partition function and obtain generalization guarantees for our method. We demonstrate that randomization can not only improve efficiency but also generalization to unseen data.
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Brouillard, Philippe. "Apprentissage de modèles causaux par réseaux de neurones artificiels." Thesis, 2020. http://hdl.handle.net/1866/25096.
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Dans ce mémoire par articles, nous nous intéressons à l’apprentissage de modèles causaux à
partir de données. L’intérêt de cette entreprise est d’obtenir une meilleure compréhension
des données et de pouvoir prédire l’effet qu’aura un changement sur certaines variables d’un
système étudié. Comme la découverte de liens causaux est fondamentale en sciences, les
méthodes permettant l’apprentissage de modèles causaux peuvent avoir des applications
dans une pléthore de domaines scientifiques, dont la génomique, la biologie et l’économie.
Nous présentons deux nouvelles méthodes qui ont la particularité d’être des méthodes
non-linéaires d’apprentissage de modèles causaux qui sont posées sous forme d’un problème
d’optimisation continue sous contrainte. Auparavant, les méthodes d’apprentissage de mo-
dèles causaux abordaient le problème de recherche de graphes en utilisant des stratégies de
recherche voraces. Récemment, l’introduction d’une contrainte d’acyclicité a permis d’abor-
der le problème différemment.
Dans un premier article, nous présentons une de ces méthodes: GraN-DAG. Sous cer-
taines hypothèses, GraN-DAG permet d’apprendre des graphes causaux à partir de données
observationnelles. Depuis la publication du premier article, plusieurs méthodes alternatives
ont été proposées par la communauté pour apprendre des graphes causaux en posant aussi
le problème sous forme d’optimisation continue avec contrainte. Cependant, aucune de ces
méthodes ne supportent les données interventionnelles. Pourtant, les interventions réduisent
le problème d’identifiabilité et permettent donc l’utilisation d’architectures neuronales plus
expressives. Dans le second article, nous présentons une autre méthode, DCDI, qui a la
particularité de pouvoir utiliser des données avec différents types d’interventions. Comme
le problème d’identifiabilité est moins important, une des deux instanciations de DCDI est
un approximateur de densité universel. Pour les deux méthodes proposées, nous montrons
que ces méthodes ont de très bonnes performances sur des données synthétiques et réelles
comparativement aux méthodes traditionelles.In this thesis by articles, we study the learning of causal models from data. The goal of this entreprise is to gain a better understanding of data and to be able to predict the effect of a change on some variables of a given system. Since discovering causal relationships is fundamental in science, causal structure learning methods have applications in many fields that range from genomics, biology, and economy. We present two new methods that have the particularity of being non-linear methods learning causal models casted as a continuous optimization problem subject to a constraint. Previously, causal strutural methods addressed this search problem by using greedy search heuristics. Recently, a new continuous acyclity constraint has allowed to address the problem differently. In the first article, we present one of these non-linear method: GraN-DAG. Under some assumptions, GraN-DAG can learn a causal graph from observational data. Since the publi- cation of this first article, several alternatives methods have been proposed by the community by using the same continuous-constrained optimization formulation. However, none of these methods support interventional data. Nevertheless, interventions reduce the identifiability problem and allow the use of more expressive neural architectures. In the second article, we present another method, DCDI, that has the particularity to leverage data with several kinds of interventions. Since the identifiabiliy issue is less severe, one of the two instantia- tions of DCDI is a universal density approximator. For both methods, we show that these methods have really good performances on synthetic and real-world tasks comparatively to other classical methods.
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Bergman, Ruth. "Learning World Models in Environments with Manifest Causal Structure." 1995. http://hdl.handle.net/1721.1/6777.
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This thesis examines the problem of an autonomous agent learning a causal world model of its environment. Previous approaches to learning causal world models have concentrated on environments that are too "easy" (deterministic finite state machines) or too "hard" (containing much hidden state). We describe a new domain --- environments with manifest causal structure --- for learning. In such environments the agent has an abundance of perceptions of its environment. Specifically, it perceives almost all the relevant information it needs to understand the environment. Many environments of interest have manifest causal structure and we show that an agent can learn the manifest aspects of these environments quickly using straightforward learning techniques. We present a new algorithm to learn a rule-based causal world model from observations in the environment. The learning algorithm includes (1) a low level rule-learning algorithm that converges on a good set of specific rules, (2) a concept learning algorithm that learns concepts by finding completely correlated perceptions, and (3) an algorithm that learns general rules. In addition this thesis examines the problem of finding a good expert from a sequence of experts. Each expert has an "error rate"; we wish to find an expert with a low error rate. However, each expert's error rate and the distribution of error rates are unknown. A new expert-finding algorithm is presented and an upper bound on the expected error rate of the expert is derived.
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Kowalchuk, Rhonda K. D. "A causal structural model for the analysis of desired family size." 1993. http://hdl.handle.net/1993/28822.
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Robitaille-Grou, Marie-Christine. "Biais écologique de la méta-analyse avec modificateur d'effet sous le paradigme de l'inférence causale." Thèse, 2017. http://hdl.handle.net/1866/20209.
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Barker, Katrina L., University of Western Sydney, College of Arts, and School of Education. "Specifying causal relations between students' goals and academic self-concept: an integrated structural model of student motivation." 2006. http://handle.uws.edu.au:8081/1959.7/17644.
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The central aim of this thesis was to investigate relationships between students’ goals and self-concepts and to demonstrate how these two sets of motivational variables interact to influence academic achievement. Answers were, thus, sought for vexed questions concerning the causal ordering of students’ goal orientations, academic self-concepts and academic achievement by hypothesising three competing models of causality: a/ goal orientations affect academic self-concepts, which in turn affect subsequent academic achievement, b/ academic self-concept affect goal orientations, which in turn affect subsequent academic achievement, and c/ goal orientations, academic self-concepts, and academic achievement affect each other such as they are reciprocally related over time. Findings from this research hold important implications for our theoretical understanding of factors affecting student motivation, and also for educational practice and research relating to students’ goals and academic self-concepts. These implications, in turn, provide new perspectives for promoting optimal motivation and academic achievement amongst secondary school students.Doctor of Philosophy (PhD)
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MI, Sung Chyou, and 宋秋美. "The Verification of the Causal Model of 4-Dimentional goal orientation, including moderating effects of classroom goal structure." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/90493863389254095432.
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博士國立臺灣師範大學
教育學系
95
According to new trend of achievement goal theory, and social cognition theories that learning motivation is influenced by interplay between personal and contextual goals, in this dissertation it was proposed a causal model of 4-dimensional goal orientation to explain the causal relationship between latent independent variables and latent dependent variables, meanwhile, by comparison of the significant differences of structural coefficients of multi-groups to analyze moderating effects of the latent variables in the said causal model due to classroom goal structures. For these purposes, this study adopted a questionnaire survey in an intended cluster sampling. The sampling was first selected from twelve districts of Taipei, second randomly selected 1-3 public or private senior high schools and then randomly selected 1-3 classes of those schools, total subjects 1261 2nd graders. The instruments employed in this study include self-efficacy inventory, intelligence incremental theory inventory, 4-dimentional goal orientation inventory, deep English learning strategy inventory and English achievement test. The data were analyzed via descriptive statistics, structure equation model (SEM) and multi-sample analysis. The conclusion of this study are as follows- First, the proposed theoretical causal model, including predicting variables- self-efficacy and intelligence incremental belief, mediating variables-4-dimension goal orientations and deep English learning strategies and final dependent variable- English achievement test, was verified fitting the empirically observed data well, either with overall or internal structure fit criteria. This results showed this causal model can explain English learning for the majority of students from Taipei senior high schools. Second, self-efficacy affects the choice of goals, having high direct and indirect effects towords deep English learning strategies and English achievement test. Third, verifying that intelligence incremental belief affects mastery approach goal and mastery avoidance goal. Fourth, 4-dimentional goal orientations have different direct effects towards deep English learning strategies, indicating supporting the theories of 4-dimentional goal orientations that Elliot和McGregor（2001）and Pintrich（2000a, 2000c）proposed and verifying the existence of mastery avoidance goal. Performance approach goal has the most direct effect towards deep English learning strategies. This result is probably due to the subjects of this research holding multiple goals, including mastery approach goal and performance approach goal. Fifth, verifying that performance approach goal and performance avoidance goal have direct effects toward English achievement test. Sixth, after multi-sample analysis, the results indicated that classroom goal structures can moderate the direct effects (β52、β53、β54)of mastery avoidance goal, performance approach goal and performance avoidance goals to deep English learning strategies and the direct effect (β64) of deep English learning strategies to English achievement test. As to the direct effect (β52)of mastery avoidance goal to deep English learning strategies, high mastery/low performance classroom was more beneficial than high performance/low mastery classroom; as to the direct effects(β53、β54) of performance approach goal and performance avoidance goals to deep English learning strategies, high mastery/high performance classroom was more beneficial to than high performance/low mastery classroom; as to the direct effect (β64) of deep English learning strategies to English achievement test, high mastery/low performance classroom was more beneficial than high performance/low mastery classroom and high mastery/high performance classroom. Such results supported Normative Goal Theory but didn’t support Revised Goal Theory. Finally, as to the direct effect (β65) of deep English learning strategies to English achievement test, high mastery/high performance classroom was more beneficial than high mastery/low performance classroom and high performance/low mastery classroom. Such results supported personal multiple goal theory and extended it to classroom context. According to the above results, implications for instruction and future research are discussed.
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So, Florence. "Modelling causality in law = Modélisation de la causalité en droit." Thesis, 2020. http://hdl.handle.net/1866/25170.
Full textAbstract:
L'intérêt en apprentissage machine pour étudier la causalité s'est considérablement accru ces
dernières années. Cette approche est cependant encore peu répandue dans le domaine de
l’intelligence artificielle (IA) et du droit. Elle devrait l'être. L'approche associative actuelle
d’apprentissage machine révèle certaines limites que l'analyse causale peut surmonter. Cette
thèse vise à découvrir si les modèles causaux peuvent être utilisés en IA et droit.
Nous procédons à une brève revue sur le raisonnement et la causalité en science et en droit.
Traditionnellement, les cadres normatifs du raisonnement étaient la logique et la rationalité, mais
la théorie duale démontre que la prise de décision humaine dépend de nombreux facteurs qui
défient la rationalité. À ce titre, des statistiques et des probabilités étaient nécessaires pour
améliorer la prédiction des résultats décisionnels. En droit, les cadres de causalité ont été définis
par des décisions historiques, mais la plupart des modèles d’aujourd’hui de l'IA et droit
n'impliquent pas d'analyse causale. Nous fournissons un bref résumé de ces modèles, puis
appliquons le langage structurel de Judea Pearl et les définitions Halpern-Pearl de la causalité
pour modéliser quelques décisions juridiques canadiennes qui impliquent la causalité.
Les résultats suggèrent qu'il est non seulement possible d'utiliser des modèles de causalité
formels pour décrire les décisions juridiques, mais également utile car un schéma uniforme
élimine l'ambiguïté. De plus, les cadres de causalité sont utiles pour promouvoir la
responsabilisation et minimiser les biais.The machine learning community’s interest in causality has significantly increased in recent years. This trend has not yet been made popular in AI & Law. It should be because the current associative ML approach reveals certain limitations that causal analysis may overcome. This research paper aims to discover whether formal causal frameworks can be used in AI & Law. We proceed with a brief account of scholarship on reasoning and causality in science and in law. Traditionally, normative frameworks for reasoning have been logic and rationality, but the dual theory has shown that human decision-making depends on many factors that defy rationality. As such, statistics and probability were called for to improve the prediction of decisional outcomes. In law, causal frameworks have been defined by landmark decisions but most of the AI & Law models today do not involve causal analysis. We provide a brief summary of these models and then attempt to apply Judea Pearl’s structural language and the Halpern-Pearl definitions of actual causality to model a few Canadian legal decisions that involve causality. Results suggest that it is not only possible to use formal causal models to describe legal decisions, but also useful because a uniform schema eliminates ambiguity. Also, causal frameworks are helpful in promoting accountability and minimizing biases.
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Chang, Fang-Ming, and 張芳銘. "Using Structural Equation Modeling to Construct the Multiple Indicators and Multiple Causes Model in ADHD:Repeated Measurements Data." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/36439n.
Full textAbstract:
碩士淡江大學
數學學系碩士班
101
Attention-Deficit Hyperactivity Disorder (ADHD) is one of children''s most common neural behavioral disorders. Due to the various situations of ADHD child, the method which the doctor adopts in treating ADHD will also be adjusted on the basis of demand for the case. One of the main reasons is that an ADHD child has not only various potential risk factors from individual, family and/or school, but also is heavily comorbid .On the other hands, the personal characteristics of parent will influence the conditions of ADHD child and/or the comorbidity. According to the aforementioned multiple indicators and multiple causes (MIMIC) problems, we will try to use the structural equation modeling (SEM) method to conduct the causal relationships among them. The results will help the children psychiatrist to establish an effective treatment plan for each ADHD child.
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Zheng, YING. "CHINESE UNIVERSITY STUDENTS’ MOTIVATION, ANXIETY, GLOBAL AWARENESS, LINGUISTIC CONFIDENCE, AND ENGLISH TEST PERFORMANCE: A CORRELATIONAL AND CAUSAL INVESTIGATION." Thesis, 2009. http://hdl.handle.net/1974/5378.
Full textAbstract:
This study examined motivation, anxiety, global awareness, and linguistic confidence, and their relation to language test performance within the context of Chinese university students taking the College English Test Band 4(CET-4) in China. Using a mixed methods approach, through survey and interview inquiries, this study explored whether and how the selected psychological factors contributed to students’ CET performance. Results from exploratory factor analysis revealed that Chinese university students displayed three types of instrumental motivation (i.e., mark orientation, further-education orientation, and job orientation), two types of anxiety (i.e., language anxiety and test anxiety), and two types of confidence (i.e., linguistic confidence and test confidence). The results of confirmatory factor analysis led to a modified socio-educational model of motivation with some context-specific concepts (i.e., new instrumental orientations, global awareness, and linguistic confidence) that more accurately represented the characteristics of the Chinese university students. The results of structural equation modelling confirmed that attitude toward the learning situation and integrative orientation were two strong indicators of motivation, which in turn influenced language achievement and confidence. The negative impact of anxiety on language achievement was confirmed. Certain group differences were found in comparing male students with female students, high achievers with low achievers, students from the Arts programs with those from the Science programs, and students who started to learn English before Grade 7 with those
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who did so after Grade 7. The interview findings indicated stronger instrumental orientations than integrative orientations. External influences, including influences from society, teachers, and peers, were also identified. Students expressed their mixed feelings toward the CET-4, indicating that this test had both positive and negative influences in promoting their English learning. Testing well-developed motivation and anxiety models in the Chinese context enriched and expanded our knowledge in theory development in English language education in China. The implications of this study point to the importance of understanding language test-takers’ characteristics in their macro and micro learning contexts, as well as the importance of establishing the relevance of English language learning to language teaching, and testing in English as Foreign Language contexts.Thesis (Ph.D, Education) -- Queen's University, 2009-12-30 22:08:41.138
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Halbe, Johannes. "Governance of Transformations towards Sustainable Water, Food and Energy Supply Systems - Facilitating Sustainability Innovations through Multi-Level Learning Processes." Doctoral thesis, 2017. https://repositorium.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-2017022715609.
Full textAbstract:
A fundamental change in societal values and economic structures is required to address increasing pressures on ecosystems and natural resources. Transition research has developed in the last decades to analyze the co-dynamics of technological, institutional, social and economic elements in the provision of key functions such as energy, water and food supply. This doctoral dissertation provides conceptual and methodological contributions to the pro-active governance of sustainability transitions. Three research gaps are identified that are addressed in this dissertation. First, a comprehensive conceptualization of learning in sustainability transitions is currently missing that comprises learning at multiple societal levels (ranging from individuals to policy-actors). Learning concepts are often not explicitly discussed in transition research even though learning is considered as fundamental for innovation processes, niche formation and development as well as breakthrough and diffusion of innovations. Second, methods for the analysis and design of transition governance processes are lacking that specify case-specific intervention points and roles of actors in the implementation of innovations. Third, participatory modeling approaches are only applied to a limited extent in transition research despite a high potential for supporting communication and learning.
The conceptualization of multi-level learning developed in this doctoral research conceptualizes learning at different societal levels as specific learning contexts ranging from individual and group contexts to organizational and policy contexts. The conceptual framework further differentiates between learning processes, intensity, objects, outcomes, subjects and factors, allowing for a more detailed analysis of learning within and across learning contexts. Thus, learning contexts can be linked by processes that involve actors from different learning contexts (e.g., community groups and policy-makers), as well as exchanges of physical aspects, institutions and knowledge (in the form of ‘learning factors’). This research has also provided a classification of model uses in transition research that supports a purposeful discussion of the opportunities of modeling and promising future research directions.
The methodology developed in this doctoral research aims at the analysis and design of transition governance processes by specifying the various opportunities to contribute to sustainability transitions through purposeful action at different societal levels, as well as related roles of stakeholders in implementing such processes of change. The methodology combines different streams of previous research: 1) a participatory modeling approach to identify problem perceptions, case-specific sustainability innovations as well as related implementation barriers, drivers and responsibilities; 2) a systematic review to identify supportive and impeding learning factors from the general literature that can complement case-specific factors; and 3) a method for the analysis and design of case-specific transition governance processes. Three case studies in Canada (topic: sustainable food systems), Cyprus (water-energy-food nexus) and Germany (sustainable heating supply) have been selected to test and iteratively develop the methodology described above.
The results for each case study reveal that there are learning objects (i.e., learning requirements) in all learning contexts, which underscores the importance of multi-level learning in sustainability transitions, ranging from the individual to the group, organizational and policy levels. Actors have various opportunities to actively facilitate societal transformations towards sustainable development either directly through actions at their particular societal levels (i.e., context-internal learning) or indirectly through actions that influence learning at other societal levels. In fact, most of the learning factors require cooperation across learning contexts during the implementation process. The comparing of learning factors across case studies underline the importance of several factor categories, such as ‘physical a ‘disturbance or crisis’, ‘information and knowledge’. Of the 206 factors identified by stakeholders, 40 factors are case-specific and not contained in the general, review-based factor list. This underscores the value of participatory research, as general, top-down analyses might have overlooked these case-specific factors.
The methodology presented in this dissertation allows for the identification and analysis of case-specific intervention points for sustainability transitions at multiple societal levels. The methodology furthermore permits the analysis of interplay between individual, group, organizational and policy actions, which is a first step towards their coordination. The focus on sustainability innovations links the broad topic of sustainability transitions to a set of opportunities for practical interventions and overcoming their implementation barriers. The methodology presented allows for the analysis and design of these interlinkages between learning contexts. While the methodology cannot provide any ‘silver bullets’ for inducing sustainability transitions, it is flexible enough to identify an appropriate abstraction level for analyzing and designing transition governance processes. The methodology developed in this doctoral research also provides several contributions for the development of participatory modeling methods in transition research. Thus, the participatory method supports an integrated analysis of barriers and drivers of sustainability innovations, and allows application in practice and education.
The concepts and methods developed in this research project allow for reflection on transition governance processes from a systemic viewpoint. Experiences in the case studies underline the applicability of the concepts and methods developed for the analysis of case-specific transition governance processes. Despite substantial differences in the geographic location, culture and topics addressed, all case studies include promising sustainability innovations and the engagement of multiple actors in their implementation. The diversity and multitude of initiatives in the case study regions provides an optimistic outlook on future opportunities for large-scale sustainability transitions.