Dissertations / Theses on the topic 'Structural causal models'
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Oberst, Michael Karl. "Counterfactual policy introspection using structural causal models." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/124128.
Full textThesis: 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
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.
Full textAten, 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.
Full textEwings, 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/.
Full textRosich, 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.
Full textEl 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.
Dubois, Florent. "Dynamic models of segregation." Thesis, Aix-Marseille, 2017. http://www.theses.fr/2017AIXM0313.
Full textThis 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
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.
Full textBailly, Sébastien. "Utilisation des antifongiques chez le patient non neutropénique en réanimation." Thesis, Université Grenoble Alpes (ComUE), 2015. http://www.theses.fr/2015GREAS013/document.
Full textCandida 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
Bergman, Ruth. "Learning models of environments with manifest causal structure." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/36559.
Full textIncludes bibliographical references (leaves 188-192).
by Ruth Bergman.
Ph.D.
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/.
Full textBackground: 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
Suh, Youngkyoon. "Exploring Causal Factors of DBMS Thrashing." Diss., The University of Arizona, 2015. http://hdl.handle.net/10150/556213.
Full textHütter, 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.
Full textCataloged 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
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.
Full textCataloged 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
Triplett, Josh. "Relativistic Causal Ordering A Memory Model for Scalable Concurrent Data Structures." PDXScholar, 2012. https://pdxscholar.library.pdx.edu/open_access_etds/497.
Full textBoles, Myra. "A Causal Model of Hospital Volume, Structure and Process Indicators, and Surgical Outcomes." VCU Scholars Compass, 1994. https://scholarscompass.vcu.edu/etd/4370.
Full textAka, 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.
Full textKim, 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.
Full textPh. D.
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.
Full textBörsum, 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.
Full textLeung, 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.
Full textChong, 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.
Full textBrunelli, 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/.
Full textThe 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.
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.
Full textPlagnes, Valérie. "Structure et fonctionnement des aquifères karstiques : caractérisation par la géochimie des eaux." Montpellier 2, 1997. http://www.theses.fr/1997MON20193.
Full textFagua, 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.
Full textIslam, 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.
Full textTransportation Engineering
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.
Full textTypescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 104-115).
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.
Full text(5929691), Asish Ghoshal. "Efficient Algorithms for Learning Combinatorial Structures from Limited Data." Thesis, 2019.
Find full textBrouillard, Philippe. "Apprentissage de modèles causaux par réseaux de neurones artificiels." Thesis, 2020. http://hdl.handle.net/1866/25096.
Full textIn 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.
Bergman, Ruth. "Learning World Models in Environments with Manifest Causal Structure." 1995. http://hdl.handle.net/1721.1/6777.
Full textKowalchuk, Rhonda K. D. "A causal structural model for the analysis of desired family size." 1993. http://hdl.handle.net/1993/28822.
Full textRobitaille-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.
Full textBarker, 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.
Full textDoctor of Philosophy (PhD)
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.
Full text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.
So, Florence. "Modelling causality in law = Modélisation de la causalité en droit." Thesis, 2020. http://hdl.handle.net/1866/25170.
Full textThe 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.
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 text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.
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 textThesis (Ph.D, Education) -- Queen's University, 2009-12-30 22:08:41.138
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 text