Dissertations / Theses on the topic 'Causal machine learning'
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Moffett, Jeffrey P. "Applying Causal Models to Dynamic Difficulty Adjustment in Video Games." Digital WPI, 2010. https://digitalcommons.wpi.edu/etd-theses/320.
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We have developed a causal model of how various aspects of a computer game influence how much a player enjoys the experience, as well as how long the player will play. This model is organized into three layers: a generic layer that applies to any game, a refinement layer for a particular game genre, and an instantiation layer for a specific game. Two experiments using different games were performed to validate the model. The model was used to design and implement a system and API for Dynamic Difficulty Adjustment(DDA). This DDA system and API uses machine learning techniques to make changes to a game in real time in the hopes of improving the experience of the user and making them play longer. A final experiment is presented that shows the effectiveness of the designed system.
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Bethard, Steven John. "Finding event, temporal and causal structure in text: A machine learning approach." Connect to online resource, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3284435.
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Balsa, Fernández Juan José. "Using causal tree algorithms with difference in difference methodology : a way to have causal inference in machine learning." Tesis, Universidad de Chile, 2018. http://repositorio.uchile.cl/handle/2250/168527.
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TESIS PARA OPTAR AL GRADO DE MAGISTER EN ANÁLISIS ECONÓMICObeen for a long time one of the main focus of the economist around the world. At the same time, the development of different statistical methodologies have deeply helps them to complement the economic theory with the different types of data. One of the newest developments in this area is the Machine Learning algorithms for Causal inference, which gives them the possibility of using huge amounts of data, combined with computational tools for much more precise results. Nevertheless, these algorithms have not implemented one of the most used methodologies in the public evaluation, the Difference in Difference methodology. This document proposes an estimator that combines the Honest Causal Tree of Athey and Imbens (2016) with the Difference in Difference framework, giving us the opportunity to obtain heterogeneous treatment effect. Although the proposed estimator has higher levels of Bias, MSE, and Variance in comparison with the OLS, it is able to find significant results in cases where OLS do not, and instead of estimate an Average Treatment Effect, it is able to estimate a treatment effect for each individual.
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Goh, Siong Thye. "Machine learning approaches to challenging problems : interpretable imbalanced classification, interpretable density estimation, and causal inference." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119281.
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Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2018.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 111-118).
In this thesis, I address three challenging machine-learning problems. The first problem that we address is the imbalanced data problem. We propose two algorithms to handle highly imbalanced classification problems. The first algorithm uses mixed integer programming to optimize a weighted balance between positive and negative class accuracies. The second method uses an approximation in order to assist with scalability. Specifically, it follows a characterize-then-discriminate approach. The positive class is first characterized by boxes, and then each box boundary becomes a separate discriminative classifier. This method is computationally advantageous because it can be easily parallelized, and considers only the relevant regions of the feature space. The second problem is a density estimation problem for categorical data sets. We present tree- and list- structured density estimation methods for binary/categorical data. We present three generative models, where the first one allows the user to specify the number of desired leaves in the tree within a Bayesian prior. The second model allows the user to specify the desired number of branches within the prior. The third model returns lists (rather than trees) and allows the user to specify the desired number of rules and the length of rules within the prior. Finally, we present a new machine learning approach to estimate personalized treatment effects in the classical potential outcomes framework with binary outcomes. Strictly, both treatment and control outcomes must be measured for each unit in order to perform supervised learning. However, in practice, only one outcome can be observed per unit. To overcome the problem that both treatment and control outcomes for the same unit are required for supervised learning, we propose surrogate loss functions that incorporate both treatment and control data. The new surrogates yield tighter bounds than the sum of the losses for the treatment and control groups. A specific choice of loss function, namely a type of hinge loss, yields a minimax support vector machine formulation. The resulting optimization problem requires the solution to only a single convex optimization problem, incorporating both treatment and control units, and it enables the kernel trick to be used to handle nonlinear (also non-parametric) estimation.
by Siong Thye Goh.
Ph. D.
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Hagerty, Nicholas L. "Bayesian Network Modeling of Causal Relationships in Polymer Models." Miami University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=miami1619009432971036.
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Lash, Michael Timothy. "Optimizing outcomes via inverse classification." Diss., University of Iowa, 2018. https://ir.uiowa.edu/etd/6602.
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In many circumstances, predictions elicited from induced classification models are useful to a certain extent, as such predictions provide insight into what the future may hold. Such models, in and of themselves, hold little value beyond making such predictions, as they are unable to inform their user as to how to change a predicted outcome. Consider, for example, a health care domain where a classification model has been induced to learn the mapping from patient characteristics to disease outcome. A patient may want to know how to lessen their probability of developing such a disease.
In this document, four different approaches to inverse classification, the process of turning predictions into prescriptions by working backwards through an induced classification model to optimize for a particular outcome of interest, are explored. The first study develops an inverse classification framework, which is created to produce instance-specific, real-world feasible recommendations that optimally improve the probability of a good outcome, while being as classifier-permissive as possible. Real-world feasible recommendations are obtained by imposition of constraints that specify which features can be optimized over and accounts for user-specific preferences. Assumptions are made as to the differentiability of the classification function, permitting the use of classifiers with exploitable gradient information, such as support vector machines (SVMs) and logistic regression. Our results show that the framework produces real-world recommendations that successfully reduce the probability of a negative outcome.
In the second study, we further relax our assumptions as to the differentiability of the classifier, allowing virtually any classification function to be used. Correspondingly, we adjust our optimization methodology. To such an end, three heuristic-based optimization methods are devised. Furthermore, non-linear (quadratic) relationships between feature changes and so-called cost, which accounts for user preferences, are explored. The results suggest that non-differentiable classifiers, such as random forests, can be successfully navigated using the specified framework and updated, heuristic-based optimization methodology. Furthermore, findings suggest that regularizers, encouraging sparse solutions, should be used when quadratic/non-linear cost-change relationships are specified.
The third study takes a longitudinal approach to the problem, exploring the effects of applying the inverse classification process to instances across time. Furthermore, we explore the use of added temporal linkages, in the form of features representing past predicted outcome probability (i.e., risk), on the inverse classification results. We further explore and propose a solution to a missing data subproblem that frequently arises in longitudinal data settings.
In the fourth and final study, a causal formulation of the inverse classification framework is provided and explored. The formulation encompasses a Gaussian Process-based method of inducing causal classifiers, which is subsequently leveraged when the inverse classification process is applied. Furthermore, exploration of the addition of certain dependencies is explored. The results suggest the importance of including such dependencies and the benefits of taking a causal approach to the problem.
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Kaiser, Michael Rainer Johann [Verfasser], and Florian [Akademischer Betreuer] Englmaier. "From causal inference to machine learning : four essays in empirical economics / Michael Rainer Johann Kaiser ; Betreuer: Florian Englmaier." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2021. http://d-nb.info/1229835709/34.
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Miranda, Ackerman Eduardo Jacobo. "Extracting Causal Relations between News Topics from Distributed Sources." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2013. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-130066.
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The overwhelming amount of online news presents a challenge called news information overload. To mitigate this challenge we propose a system to generate a causal network of news topics. To extract this information from distributed news sources, a system called Forest was developed. Forest retrieves documents that potentially contain causal information regarding a news topic. The documents are processed at a sentence level to extract causal relations and news topic references, these are the phases used to refer to a news topic. Forest uses a machine learning approach to classify causal sentences, and then renders the potential cause and effect of the sentences. The potential cause and effect are then classified as news topic references, these are the phrases used to refer to a news topics, such as “The World Cup” or “The Financial Meltdown”. Both classifiers use an algorithm developed within our working group, the algorithm performs better than several well known classification algorithms for the aforementioned tasks.
In our evaluations we found that participants consider causal information useful to understand the news, and that while we can not extract causal information for all news topics, it is highly likely that we can extract causal relation for the most popular news topics. To evaluate the accuracy of the extractions made by Forest, we completed a user survey. We found that by providing the top ranked results, we obtained a high accuracy in extracting causal relations between news topics.
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Hazan, Amaury. "Musical expectation modelling from audio : a causal mid-level approach to predictive representation and learning of spectro-temporal events." Doctoral thesis, Universitat Pompeu Fabra, 2010. http://hdl.handle.net/10803/22721.
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We develop in this thesis a computational model of music expectation, which may be one of the most important aspects in music listening. Many phenomenons related to music listening such as preference, surprise or emo- tions are linked to the anticipatory behaviour of listeners. In this thesis, we concentrate on a statistical account to music expectation, by modelling the processes of learning and predicting spectro-temporal regularities in a causal fashion.
The principle of statistical modelling of expectation can be applied to several music representations, from symbolic notation to audio signals. We first show that computational learning architectures can be used and evaluated to account behavioral data concerning auditory perception and learning. We then propose a what/when representation of musical events which enables to sequentially describe and learn the structure of acoustic units in musical audio signals.
The proposed representation is applied to describe and anticipate timbre features and musical rhythms. We suggest ways to exploit the properties of the expectation model in music analysis tasks such as structural segmentation. We finally explore the implications of our model for interactive music applications in the context of real-time transcription, concatenative synthesis, and visualization.Esta tesis presenta un modelo computacional de expectativa musical, que es un aspecto muy importante de como procesamos la música que oímos. Muchos fenómenos relacionados con el procesamiento de la música están vinculados a una capacidad para anticipar la continuación de una pieza de música. Nos enfocaremos en un acercamiento estadístico de la expectativa musical, modelando los procesos de aprendizaje y de predicción de las regularidades espectro-temporales de forma causal. El principio de modelado estadístico de la expectativa se puede aplicar a varias representaciones de estructuras musicales, desde las notaciones simbólicas a la señales de audio. Primero demostramos que ciertos algoritmos de aprendizaje de secuencias se pueden usar y evaluar en el contexto de la percepción y el aprendizaje de secuencias auditivas. Luego, proponemos una representación, denominada qué/cuándo, para representar eventos musicales de una forma que permite describir y aprender la estructura secuencial de unidades acústicas en señales de audio musical. Aplicamos esta representación para describir y anticipar características tímbricas y ritmos. Sugerimos que se pueden explotar las propiedades del modelo de expectativa para resolver tareas de análisis como la segmentación estructural de piezas musicales. Finalmente, exploramos las implicaciones de nuestro modelo a la hora de definir nuevas aplicaciones en el contexto de la transcripción en tiempo real, la síntesis concatenativa y la visualización.
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Ziebart, Brian D. "Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy." Research Showcase @ CMU, 2010. http://repository.cmu.edu/dissertations/17.
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Predicting human behavior from a small amount of training examples is a challenging machine learning problem. In this thesis, we introduce the principle of maximum causal entropy, a general technique for applying information theory to decision-theoretic, game-theoretic, and control settings where relevant information is sequentially revealed over time. This approach guarantees decision-theoretic performance by matching purposeful measures of behavior (Abbeel & Ng, 2004), and/or enforces game-theoretic rationality constraints (Aumann, 1974), while otherwise being as uncertain as possible, which minimizes worst-case predictive log-loss (Gr¨unwald & Dawid, 2003).
We derive probabilistic models for decision, control, and multi-player game settings using this approach. We then develop corresponding algorithms for efficient inference that include relaxations of the Bellman equation (Bellman, 1957), and simple learning algorithms based on convex optimization. We apply the models and algorithms to a number of behavior prediction tasks. Specifically, we present empirical evaluations of the approach in the domains of vehicle route preference modeling using over 100,000 miles of collected taxi driving data, pedestrian motion modeling from weeks of indoor movement data, and robust prediction of game play in stochastic multi-player games.
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Kravchenko, Evgenija. "Association between cognitive measures, global brain surface area, genetics, and screen-time in young adolescents : Estimation of causal inference with machine learning." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-290033.
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Screen media activity such as watching TV and videos, playing video games, and using social media has become a popular leisure activity for children and adolescents. The effect of screen time has been a highly debated topic; however, there is still very little known about it. Using a dataset from the Adolescent Brain Cognitive Development longitudinal study 4 217 young adolescents, that met the requirements, could be retrieved for this thesis project after processing of the data. This thesis project investigated causal order between genetic effect (cognitive performance Polygenic scores (PGSs)), screen time activity, brain morphology (structural Magnetic Resonance Imaging (sMRI) for surface area and cortical thickness), lack of perseverance, and cognitive performance (crystallized IQ) with a machine learning algorithm DirectLiNGAM. A clear correlation between screen media activity and PGS was found for all types of screen time activities but only video games and social media correlated to the global surface area. Furthermore, TV and video seem to affect lack of perseverance, and lack of perseverance, in turn, affects time spent on video games. These findings imply that different types of social media are not as alike as we thought and can affect adolescents differently. Taken together, these findings support previous research on screen media activity's effect on lack of perseverance, brain morphology, and cognitive performance, and propose new causal inference between genetics and screen time. Lastly, the algorithm used in this thesis project inferred reasonable causal orders and can be seen as a very good complement to today's causal modeling.Skärmaktivitet som att titta på TV och video, spela videospel och använda sociala medier har blivit en populär fritidsaktivitet för barn och ungdomar. Effekten av skärmtid har varit ett mycket debatterat ämne; det finns dock fortfarande mycket lite kunskap om det. Med hjälp av datasetet från Adolescent Brain Cognitive Development långtidsstudien kunde 4 217 ungdomar, som uppfyllde specifika krav, väljas ut för detta avhandlingsprojekt efter bearbetning av datan. Detta avhandlingsprojekt undersökte kausal ordning mellan genetisk effekt (Polygenic scores (PGS) för kognitiv prestation), skärmtidsaktivitet, hjärnmorfologi (strukturell Magnet Resonans Imaging (sMRI) för hjärnans ytarea och hjärnbarks tjocklek), brist på ihärdighet och kognitiv förmåga (kristalliserad IQ) med en maskininlärningsalgoritm DirectLiNGAM. Tydlig korrelation mellan skärmaktivitet och PGS hittades för alla typer av skärmaktiviteter men endast videospel och sociala medier korrelerade till den globala ytarean. Dessutom verkar TV och video påverka brist på ihärdighet och brist på ihärdighet i sin tur påverkar hur mycket tid som spenderas på videospel. Dessa resultat antyder att olika typer av sociala medier inte är så lika som vi trodde och kan påverka ungdomar olika. Sammanlagt stöder dessa upptäckter tidigare forskning om skärmtidseffekt på brist på ihärdighet, hjärnmorfologi och kognitiv förmåga och föreslår en ny kausal inferens mellan genetik och skärmtid. Slutligen ledde algoritmen som användes i detta avhandlingsprojekt fram till rimliga kausala ordningar och kan ses som ett mycket bra komplement till dagens kausala modellering.
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Olsen, Catharina. "Causal inference and prior integration in bioinformatics using information theory." Doctoral thesis, Universite Libre de Bruxelles, 2013. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209401.
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An important problem in bioinformatics is the reconstruction of gene regulatory networks from expression data. The analysis of genomic data stemming from high- throughput technologies such as microarray experiments or RNA-sequencing faces several difficulties. The first major issue is the high variable to sample ratio which is due to a number of factors: a single experiment captures all genes while the number of experiments is restricted by the experiment’s cost, time and patient cohort size. The second problem is that these data sets typically exhibit high amounts of noise.Another important problem in bioinformatics is the question of how the inferred networks’ quality can be evaluated. The current best practice is a two step procedure. In the first step, the highest scoring interactions are compared to known interactions stored in biological databases. The inferred networks passes this quality assessment if there is a large overlap with the known interactions. In this case, a second step is carried out in which unknown but high scoring and thus promising new interactions are validated ’by hand’ via laboratory experiments. Unfortunately when integrating prior knowledge in the inference procedure, this validation procedure would be biased by using the same information in both the inference and the validation. Therefore, it would no longer allow an independent validation of the resulting network.
The main contribution of this thesis is a complete computational framework that uses experimental knock down data in a cross-validation scheme to both infer and validate directed networks. Its components are i) a method that integrates genomic data and prior knowledge to infer directed networks, ii) its implementation in an R/Bioconductor package and iii) a web application to retrieve prior knowledge from PubMed abstracts and biological databases. To infer directed networks from genomic data and prior knowledge, we propose a two step procedure: First, we adapt the pairwise feature selection strategy mRMR to integrate prior knowledge in order to obtain the network’s skeleton. Then for the subsequent orientation phase of the algorithm, we extend a criterion based on interaction information to include prior knowledge. The implementation of this method is available both as part of the prior retrieval tool Predictive Networks and as a stand-alone R/Bioconductor package named predictionet.
Furthermore, we propose a fully data-driven quantitative validation of such directed networks using experimental knock-down data: We start by identifying the set of genes that was truly affected by the perturbation experiment. The rationale of our validation procedure is that these truly affected genes should also be part of the perturbed gene’s childhood in the inferred network. Consequently, we can compute a performance score
Doctorat en Sciences
info:eu-repo/semantics/nonPublished
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Abar, Orhan. "Rule Mining and Sequential Pattern Based Predictive Modeling with EMR Data." UKnowledge, 2019. https://uknowledge.uky.edu/cs_etds/85.
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Electronic medical record (EMR) data is collected on a daily basis at hospitals and other healthcare facilities to track patients’ health situations including conditions, treatments (medications, procedures), diagnostics (labs) and associated healthcare operations. Besides being useful for individual patient care and hospital operations (e.g., billing, triaging), EMRs can also be exploited for secondary data analyses to glean discriminative patterns that hold across patient cohorts for different phenotypes. These patterns in turn can yield high level insights into disease progression with interventional potential. In this dissertation, using a large scale realistic EMR dataset of over one million patients visiting University of Kentucky healthcare facilities, we explore data mining and machine learning methods for association rule (AR) mining and predictive modeling with mood and anxiety disorders as use-cases. Our first work involves analysis of existing quantitative measures of rule interestingness to assess how they align with a practicing psychiatrist’s sense of novelty/surprise corresponding to ARs identified from EMRs. Our second effort involves mining causal ARs with depression and anxiety disorders as target conditions through matching methods accounting for computationally identified confounding attributes. Our final effort involves efficient implementation (via GPUs) and application of contrast pattern mining to predictive modeling for mental conditions using various representational methods and recurrent neural networks. Overall, we demonstrate the effectiveness of rule mining methods in secondary analyses of EMR data for identifying causal associations and building predictive models for diseases.
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Meganck, Stijn. "Towards an Integral Approach for Modeling Causality." Phd thesis, INSA de Rouen, 2008. http://tel.archives-ouvertes.fr/tel-00915256.
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A partir de données d'observation classiques, il est rarement possible d'arriver à une structure de réseau bayésien qui soit complètement causale. Le point théorique auquel nous nous intéressons est l'apprentissage des réseaux bayésiens causaux, avec ou sans variables latentes. Nous nous sommes d'abord focalisés sur la découverte de relations causales lorsque toutes les variables sont connues (i.e. il n'y a pas de variables latentes) en proposant un algorithme d'apprentissage utilisant à la fois des données issues d'observations et d'expérimentations. Logiquement, nous nous sommes ensuite concentrés sur le même problème lorsque toutes les variables ne sont pas connues. Il faut donc découvrir à la fois des relations de causalité entre les variables et la présence éventuelle de variables latentes dans la structure du réseau bayésien. Pour cela, nous tentons d'unifier deux formalismes, les modèles causaux semi-markoviens (SMCM) et les graphes ancestraux maximaux (MAG), utilisés séparément auparavant, l'un pour l'inférence causale (SMCM), l'autre pour la découverte de causalité (MAG). Nous nous sommes aussi interessé à l'adaptation de réseaux bayésiens causaux pour des systèmes multi-agents, et sur l'apprentissage de ces modèles causaux multi-agents (MACM).
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Fox-Roberts, Patrick Kirk. "An examination of the causes of bias in semi-supervised learning." Thesis, University of Cambridge, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.648460.
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Bequé, Artem. "Verfahren des maschinellen Lernens zur Entscheidungsunterstützung." Doctoral thesis, Humboldt-Universität zu Berlin, 2018. http://dx.doi.org/10.18452/19421.
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Erfolgreiche Unternehmen denken intensiv über den eigentlichen Nutzen ihres Unternehmens für Kunden nach. Diese versuchen, ihrer Konkurrenz voraus zu sein, und zwar durch gute Ideen, Innovationen und Kreativität. Dabei wird Erfolg anhand von Metriken gemessen, wie z.B. der Anzahl der loyalen Kunden oder der Anzahl der Käufer. Gegeben, dass der Wettbewerb durch die Globalisierung, Deregulierung und technologische Innovation in den letzten Jahren angewachsen ist, spielen die richtigen Entscheidungen für den Erfolg gerade im operativen Geschäft der sämtlichen Bereiche des Unternehmens eine zentrale Rolle. Vor diesem Hintergrund entstammen die in der vorliegenden Arbeit zur Evaluation der Methoden des maschinellen Lernens untersuchten Entscheidungsprobleme vornehmlich der Entscheidungsunterstützung. Hierzu gehören Klassifikationsprobleme wie die Kreditwürdigkeitsprüfung im Bereich Credit Scoring und die Effizienz der Marketing Campaigns im Bereich Direktmarketing. In diesem Kontext ergaben sich Fragestellungen für die korrelativen Modelle, nämlich die Untersuchung der Eignung der Verfahren des maschinellen Lernens für den Bereich des Credit Scoring, die Kalibrierung der Wahrscheinlichkeiten, welche mithilfe von Verfahren des maschinellen Lernens erzeugt werden sowie die Konzeption und Umsetzung einer Synergie-Heuristik zwischen den Methoden der klassischen Statistik und Verfahren des maschinellen Lernens. Desweiteren wurden kausale Modelle für den Bereich Direktmarketing (sog. Uplift-Effekte) angesprochen. Diese Themen wurden im Rahmen von breit angelegten empirischen Studien bearbeitet. Zusammenfassend ergibt sich, dass der Einsatz der untersuchten Verfahren beim derzeitigen Stand der Forschung zur Lösung praxisrelevanter Entscheidungsprobleme sowie spezifischer Fragestellungen, welche aus den besonderen Anforderungen der betrachteten Anwendungen abgeleitet wurden, einen wesentlichen Beitrag leistet.Nowadays right decisions, being it strategic or operative, are important for every company, since these contribute directly to an overall success. This success can be measured based on quantitative metrics, for example, by the number of loyal customers or the number of incremental purchases. These decisions are typically made based on the historical data that relates to all functions of the company in general and to customers in particular. Thus, companies seek to analyze this data and apply obtained knowlegde in decision making. Classification problems represent an example of such decisions. Classification problems are best solved, when techniques of classical statistics and these of machine learning are applied, since both of them are able to analyze huge amount of data, to detect dependencies of the data patterns, and to produce probability, which represents the basis for the decision making. I apply these techniques and examine their suitability based on correlative models for decision making in credit scoring and further extend the work by causal predictive models for direct marketing. In detail, I analyze the suitability of techniques of machine learning for credit scoring alongside multiple dimensions, I examine the ability to produce calibrated probabilities and apply techniques to improve the probability estimations. I further develop and propose a synergy heuristic between the methods of classical statistics and techniques of machine learning to improve the prediction quality of the former, and finally apply conversion models to turn machine learning techqiques to account for causal relationship between marketing campaigns and customer behavior in direct marketing. The work has shown that the techniques of machine learning represent a suitable alternative to the methods of classical statistics for decision making and should be considered not only in research but also should find their practical application in real-world practices.
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Valente, Marica. "Essays on Applied Microeconomics." Doctoral thesis, Humboldt-Universität zu Berlin, 2021. http://dx.doi.org/10.18452/22184.
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In der ökonomischen Forschung wird eine Vielzahl von Strategien verwendet, um zu versuchen kausale Schlussfolgerungen aus Beobachtungsdaten zu ziehen. Neue Strömungen in der Literatur zu kausaler Inferenz konzentrieren sich auf die Kombination von Methoden zur Vorhersage und kausalen Fragestellungen. Diese neuen Methoden ermöglichen es neue Forschungsfragen zu beantworten und bieten die Möglichkeit bestehende Forschungsfragen in der Literatur neu zu adressieren. Diese Dissertation umfasst empirische Arbeiten in den Bereichen (i) Umweltökonomie: Ich evaluiere die Preispolitik für Abfälle mithilfe der “synthetic control” Methode und Methoden des maschinellen Lernens; (ii) Arbeits- und Migrationsökonomie: Ich identifiziere und quantifiziere nicht gemeldete landwirtschaftliche Arbeitsleistung, die durch einen plötzlichen Migrationszustrom verursacht wird; (iii) Konfliktökonomie: Ich analysiere die wirtschaftlichen Kosten eines hybriden Krieges, des Donbass-Krieges in der Ukraine. Der Beitrag dieser Dissertation zur bestehenden Literatur ist dreifach. Erstens kombiniere ich neuartige Datenquellen und stelle neue Datensätze bereit. Zweitens verwende ich moderne Evaluierungsmethoden und passe sie an, um politisch relevante kausale Parameter in verschiedenen Bereichen der ökonomischen Forschung abzuschätzen. Drittens vergleiche ich neuere mit traditionellen ökonometrischen Ansätzen, die zuvor in der Literatur verwendet wurden. Meine Dissertation zeigt, dass moderne ökonometrische Techniken vielversprechend sind, um die Genauigkeit und Glaubwürdigkeit von kausalen Schlussfolgerungen und die Evaluierung von Politikmassnahmen zu verbessern.In economics, researchers use a wide variety of strategies for attempting to draw causal inference from observational data. New developments in the causal inference literature focus on the combination of predictive methods and causal questions. These methods allow researchers to answer new research questions as well as provide new opportunities to address older research question in the literature. This dissertation entails empirical work in the fields of (i) environmental economics: I evaluate waste pricing policies using synthetic controls and machine learning methods; (ii) labor and migration economics: I identify and quantify unreported farm labor induced by a sudden migrant inflow; (iii) conflict economics: I evaluate the economic costs of an hybrid war, namely, the Donbass war in Ukraine. The contribution of this dissertation is threefold. First, I combine novel data sources and provide unique datasets. Second, I apply and tailor modern evaluation methods to the estimation of policy-relevant causal parameters in various fields of economics. Third, I compare recent versus traditional econometric approaches previously employed by the literature. My dissertation shows that modern econometric techniques hold great promise for improving the accuracy and credibility of causal inference and policy evaluation.
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Chapala, Usha Kiran, and Sridhar Peteti. "Continuous Video Quality of Experience Modelling using Machine Learning Model Trees." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 1996. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-17814.
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Adaptive video streaming is perpetually influenced by unpredictable network conditions, whichcauses playback interruptions like stalling, rebuffering and video bit rate fluctuations. Thisleads to potential degradation of end-user Quality of Experience (QoE) and may make userchurn from the service. Video QoE modelling that precisely predicts the end users QoE underthese unstable conditions is taken into consideration quickly. The root cause analysis for thesedegradations is required for the service provider. These sudden changes in trend are not visiblefrom monitoring the data from the underlying network service. Thus, this is challenging toknow this change and model the instantaneous QoE. For this modelling continuous time, QoEratings are taken into consideration rather than the overall end QoE rating per video. To reducethe user risk of churning the network providers should give the best quality to the users. In this thesis, we proposed the QoE modelling to analyze the user reactions change over timeusing machine learning models. The machine learning models are used to predict the QoEratings and change patterns in ratings. We test the model on video Quality dataset availablepublicly which contains the user subjective QoE ratings for the network distortions. M5P modeltree algorithm is used for the prediction of user ratings over time. M5P model gives themathematical equations and leads to more insights by given equations. Results of the algorithmshow that model tree is a good approach for the prediction of the continuous QoE and to detectchange points of ratings. It is shown that to which extent these algorithms are used to estimatechanges. The analysis of model provides valuable insights by analyzing exponential transitionsbetween different level of predicted ratings. The outcome provided by the analysis explains theuser behavior when the quality decreases the user ratings decrease faster than the increase inquality with time. The earlier work on the exponential transitions of instantaneous QoE overtime is supported by the model tree to the user reaction to sudden changes such as video freezes.
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Giuzio, Antonio. "Machine Learning per la predizione dell’outcome riabilitativo e per la scelta della componentistica protesica in pazienti con amputazione transfemorale: alberi decisionali e alberi causali." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.
Find full textAbstract:
Il protocollo clinico circa le indicazioni sull’assegnazione di protesi per amputati di arto inferiore con amputazione transfemorale risulta insufficiente e non è sostenuto da criteri di appropriatezza clinica basati sull’evidenza.
L’assegnazione delle protesi è, sinora, soggetta ai criteri stabiliti da un’equipe multidisciplinare.
Oltre a ciò, non esiste nessun tipo di strumento capace di prevedere l’evoluzione della performance fisica del paziente nel percorso di riabilitazione.
Il progetto MOTU - nato dalla collaborazione tra l’Istituto di BioRobotica della Scuola Superiore Sant’Anna di Pisa, la Fondazione Don Gnocchi di Firenze e il Dipartimento di Ingegneria dell’Energia Elettrica e dell’Informazione “Guglielmo Marconi” dell’Università di Bologna – ha consentito la realizzazione di un data set per uno studio retrospettivo sul patrimonio informativo a disposizione del Centro Protesi INAIL di Vigorso di Budrio. Tale data set costituisce la base ideale su cui implementare un recommender system capace di predire outcome riabilitativi e specifici criteri di assegnazione dei diversi sistemi protesici.
Nell’ambito di questa tesi sono stati implementati tre recommender systems diversi, basati sugli algoritmi CART, albero causale adattivo e albero causale onesto.
Il mean squared error (MSE) riferito all’albero CART ha ottenuto valori ottimali utilizzando come features tutti i valori dei test e le diverse categorie di ginocchio.
Tramite i due alberi causali sono stati ricavati risultati sugli effetti dei trattamenti considerati (protesi elettronica e protesi non elettronica).
L’albero causale adattivo ha prodotto eterogeneità dei trattamenti su tutti i data set diversi utilizzati, mostrando un MSE minimo per il modello creato tramite l’amputee mobility predictor (AMP) in ingresso come feature.
L’albero causale onesto però, non è riuscito a individuare sottogruppi utilizzando tutti i diversi data set in ingresso, non specificando nessuna eterogeneità dei trattamenti.
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Delacruz, Gian P. "Using Generative Adversarial Networks to Classify Structural Damage Caused by Earthquakes." DigitalCommons@CalPoly, 2020. https://digitalcommons.calpoly.edu/theses/2158.
Full textAbstract:
The amount of structural damage image data produced in the aftermath of an earthquake can be staggering. It is challenging for a few human volunteers to efficiently filter and tag these images with meaningful damage information. There are several solution to automate post-earthquake reconnaissance image tagging using Machine Learning (ML) solutions to classify each occurrence of damage per building material and structural member type. ML algorithms are data driven; improving with increased training data. Thanks to the vast amount of data available and advances in computer architectures, ML and in particular Deep Learning (DL) has become one of the most popular image classification algorithms producing results comparable to and in some cases superior to human experts. These kind of algorithms need the input images used for the training to be labeled, and even if there is a large amount of images most of them are not labeled and it takes structural engineers a large amount of time to do it. The current data earthquakes image data bases do not contain the label information or is incomplete slowing significantly the advance of a solution and are incredible difficult to search. To be able to train a ML algorithm to classify one of the structural damages it took the architecture school an entire year to gather 200 images of the specific damage. That number is clearly not enough to avoid overfitting so for this thesis we decided to generate synthetic images for the specific structural damage. In particular we attempt to use Generative Adversarial Neural Networks (GANs) to generate the synthetic images and enable the fast classification of rail and road damage caused by earthquakes. Fast classification of rail and road damage can allow for the safety of people and to better prepare the reconnaissance teams that manage recovery tasks. GANs combine classification neural networks with generative neural networks. For this thesis we will be combining a convolutional neural network (CNN) with a generative neural network. By taking a classifier trained in a GAN and modifying it to classify other images the classifier can take advantage of the GAN training without having to find more training data. The classifier trained in this way was able to achieve an 88\% accuracy score when classifying images of structural damage caused by earthquakes.
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Miller, John William. "Differentiation between causes of optic disc swelling using retinal layer shape features." Thesis, University of Iowa, 2018. https://ir.uiowa.edu/etd/6215.
Full textAbstract:
The optic disc is the region of the retina where the optic nerve exits the back of the eye. A number of conditions can cause the optic disc to swell. Papilledema, optic disc swelling caused by raised intracranial pressure (ICP), and nonarteritic anterior ischemic optic neuropathy (NAION), swelling caused by reduced blood flow to the back of the eye, are two such conditions. Rapid, accurate diagnosis of the cause of disc swelling is important, as with papilledema the underlying cause of raised ICP could potentially be life-threatening and may require immediate intervention.
The current clinical standard for diagnosing and assessing papilledema is a subjective measure based on qualitative inferences drawn from fundus images. Even with the expert training required to properly perform the assessment, measurements and results can vary significantly between clinicians. As such, the need for a rapid, accurate diagnostic tool for optic disc swelling is clear.
Shape analysis of the structures of the retina has emerged as a promising quantitative tool for distinguishing between causes of optic disc swelling. Optic disc swelling can cause the retinal surfaces to distort, taking on shapes that differ from their normal arrangement. Recent work has examined how changes in the shape of one of these surfaces, Bruch's membrane (BM), varies between different types of optic disc swelling, containing clinically-relevant information.
The inner limiting membrane (ILM), the most anterior retinal surface and furthest from BM, can take on shapes that are distinct from the more posterior layers when the optic disc becomes swollen. These unique shape characteristics have yet to be explored for their potential clinical utility. This thesis develops new shape models of the ILM.
The ultimate goal of this work is to develop noninvasive, automated diagnostic tools for clinical use. To that end, a necessary first step in establishing clinical relevance is demonstrating the utility of retinal shape information in a machine learning classifier. Retinal layer shape information and regional volume measurements acquired from spectral-domain optical coherence tomography scans from 78 patients (39 papilledema, 39 NAION) was used to train random forest classifiers to distinguish between cases of papilledema and NAION.
On average, the classifiers were able to correctly distinguish between papilledema and NAION 85.7±2.0% of the time, confirming the usefulness of retinal layer shapes for determining the cause of optic disc swelling. The results of this experiment are encouraging for future studies that will include more patients and attempt to differentiate between additional causes of optic disc edema.
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von, Hacht Johan. "Anomaly Detection for Root Cause Analysis in System Logs using Long Short-Term Memory." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301656.
Full textAbstract:
Many software systems are under test to ensure that they function as expected. Sometimes, a test can fail, and in that case, it is essential to understand the cause of the failure. However, as systems grow larger and become more complex, this task can become non-trivial and potentially take much time. Therefore, even partially, automating the process of root cause analysis can save time for the developers involved. This thesis investigates the use of a Long Short-Term Memory (LSTM) anomaly detector in system logs for root cause analysis. The implementation is evaluated in a quantitative and a qualitative experiment. The quantitative experiment evaluates the performance of the anomaly detector in terms of precision, recall, and F1 measure. Anomaly injection is used to measure these metrics since there are no labels in the data. Additionally, the LSTM is compared with a baseline model. The qualitative experiment evaluates how effective the anomaly detector could be for root cause analysis of the test failures. This was evaluated in interviews with an expert in the software system that produced the log data that the thesis uses. The results show that the LSTM anomaly detector achieved a higher F1 measure than the proposed baseline implementation thanks to its ability to detect unusual events and events happening out of order. The qualitative results indicate that the anomaly detector could be used for root cause analysis. In many of the evaluated test failures, the expert being interviewed could deduce the cause of the failure. Even if the detector did not find the exact issue, a particular part of the software might be highlighted, meaning that it produces many anomalous log messages. With this information, the expert could contact the people responsible for that part of the application for help. In conclusion, the anomaly detector automatically collects the necessary information for the expert to perform root cause analysis. As a result, it could save the expert time to perform this task. With further improvements, it could also be possible for non-experts to utilise the anomaly detector, reducing the need for an expert.Många mjukvarusystem testas för att försäkra att de fungerar som de ska. Ibland kan ett test misslyckas och i detta fall är det viktigt att förstå varför det gick fel. Detta kan bli problematiskt när mjukvarusystemen växer och blir mer komplexa eftersom att denna uppgift kan bli icke trivial och ta mycket tid. Om man skulle kunna automatisera felsökningsprocessen skulle det kunna spara mycket tid för de invloverade utvecklarna. Denna rapport undersöker användningen av en Long Short-Term Memory (LSTM) anomalidetektor för grundorsaksanalys i loggar. Implementationen utvärderas genom en kvantitativ och kvalitativ undersökning. Den kvantitativa undersökningen utvärderar prestandan av anomalidetektorn med precision, recall och F1 mått. Artificiellt insatta anomalier används för att kunna beräkna dessa mått eftersom att det inte finns etiketter i den använda datan. Implementationen jämförs också med en annan simpel anomalidetektor. Den kvalitativa undersökning utvärderar hur användbar anomalidetektorn är för grundorsaksanalys för misslyckade tester. Detta utvärderades genom intervjuer med en expert inom mjukvaran som producerade datan som användes in denna rapport. Resultaten visar att LSTM anomalidetektorn lyckades nå ett högre F1 mått jämfört med den simpla modellen. Detta tack vare att den kunde upptäcka ovanliga loggmeddelanden och loggmeddelanden som skedde i fel ordning. De kvalitativa resultaten pekar på att anomalidetektorn kan användas för grundorsaksanalys för misslyckade tester. I många av de misslyckade tester som utvärderades kunde experten hitta anledningen till att felet misslyckades genom det som hittades av anomalidetektorn. Även om detektorn inte hittade den exakta orsaken till att testet misslyckades så kan den belysa en vissa del av mjukvaran. Detta betyder att just den delen av mjukvaran producerad många anomalier i loggarna. Med denna information kan experten kontakta andra personer som känner till den delen av mjukvaran bättre för hjälp. Anomalidetektorn automatiskt den information som är viktig för att experten ska kunna utföra grundorsaksanalys. Tack vare detta kan experten spendera mindre tid på denna uppgift. Med vissa förbättringar skulle det också kunna vara möjligt för mindre erfarna utvecklare att använda anomalidetektorn. Detta minskar behovet för en expert.
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Lattimore, Finnian Rachel. "Learning how to act: making good decisions with machine learning." Phd thesis, 2017. http://hdl.handle.net/1885/144602.
Full textAbstract:
This thesis is about machine learning and statistical approaches
to decision making. How can we learn from data to anticipate the
consequence of, and optimally select, interventions or actions?
Problems such as deciding which medication to prescribe to
patients, who should be released on bail, and how much to charge
for insurance are ubiquitous, and have far reaching impacts on
our lives. There are two fundamental approaches to learning how
to act: reinforcement learning, in which an agent directly
intervenes in a system and learns from the outcome, and
observational causal inference, whereby we seek to infer the
outcome of an intervention from observing the system.
The goal of this thesis to connect and unify these key
approaches. I introduce causal bandit problems: a synthesis that
combines causal graphical models, which were developed for
observational causal inference, with multi-armed bandit problems,
which are a subset of reinforcement learning problems that are
simple enough to admit formal analysis. I show that knowledge of
the causal structure allows us to transfer information learned
about the outcome of one action to predict the outcome of an
alternate action, yielding a novel form of structure between
bandit arms that cannot be exploited by existing algorithms. I
propose an algorithm for causal bandit problems and prove bounds
on the simple regret demonstrating it is close to mini-max
optimal and better than algorithms that do not use the additional
causal information.
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Bergman, Ruth. "Learning World Models in Environments with Manifest Causal Structure." 1995. http://hdl.handle.net/1721.1/6777.
Full textAbstract:
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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Averitt, Amelia Jean. "Machine Learning Methods for Causal Inference with Observational Biomedical Data." Thesis, 2020. https://doi.org/10.7916/d8-je06-eh12.
Full textAbstract:
Causal inference -- the process of drawing a conclusion about the impact of an exposure on an outcome -- is foundational to biomedicine, where it is used to guide intervention. The current gold-standard approach for causal inference is randomized experimentation, such as randomized controlled trials (RCTs). Yet, randomized experiments, including RCTs, often enforce strict eligibility criteria that impede the generalizability of causal knowledge to the real world. Observational data, such as the electronic health record (EHR), is often regarded as a more representative source from which to generate causal knowledge. However, observational data is non-randomized, and therefore causal estimates from this source are susceptible to bias from confounders. This weakness complicates two central tasks of causal inference: the replication or evaluation of existing causal knowledge and the generation of new causal knowledge. In this dissertation I (i) address the feasibility of observational data to replicate existing causal knowledge and (ii) present new methods for the generation of causal knowledge with observational data, with a focus on the causal tasks of comparing an outcome between two cohorts and the estimation of attributable risks of exposures in a causal system.
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Er, Emrah. "Applications of machine learning to agricultural land values: prediction and causal inference." Diss., 2018. http://hdl.handle.net/2097/39313.
Full textAbstract:
Doctor of PhilosophyDepartment of Agricultural Economics
Nathan P. Hendricks
This dissertation focuses on the prediction of agricultural land values and the effects of water rights on land values using machine learning algorithms and hedonic pricing methods. I predict agricultural land values with different machine learning algorithms, including ridge regression, least absolute shrinkage and selection operator, random forests, and extreme gradient boosting methods. To analyze the causal effects of water right seniority on agricultural land values, I use the double-selection LASSO technique. The second chapter presents the data used in the dissertation. A unique set of parcel sales from Property Valuation Division of Kansas constitute the backbone of the data used in the estimation. Along with parcel sales data, I collected detailed basis, water, tax, soil, weather, and urban influence data. This chapter provides detailed explanation of various data sources and variable construction processes. The third chapter presents different machine learning models for irrigated agricultural land price predictions in Kansas. Researchers, and policymakers use different models and data sets for price prediction. Recently developed machine learning methods have the power to improve the predictive ability of the models estimated. In this chapter I estimate several machine learning models for predicting the agricultural land values in Kansas. Results indicate that the predictive power of the machine learning methods are stronger compared to standard econometric methods. Median absolute error in extreme gradient boosting estimation is 0.1312 whereas it is 0.6528 in simple OLS model. The fourth chapter examines whether water right seniority is capitalized into irrigated agricultural land values in Kansas. Using a unique data set of irrigated agricultural land sales, I analyze the causal effect of water right seniority on agricultural land values. A possible concern during the estimation of hedonic models is the omitted variable bias so we use double-selection LASSO regression and its variable selection properties to overcome the omitted variable bias. I also estimate generalized additive models to analyze the nonlinearities that may exist. Results show that water rights have a positive impact on irrigated land prices in Kansas. An additional year of water right seniority causes irrigated land value to increase nearly $17 per acre. Further analysis also suggest a nonlinear relationship between seniority and agricultural land prices.
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"Causal discovery from non-experimental data: 基於非實驗數據的因果分析." 2014. http://repository.lib.cuhk.edu.hk/en/item/cuhk-1291270.
Full textAbstract:
Chen, Zhitang.Thesis Ph.D. Chinese University of Hong Kong 2014.
Includes bibliographical references (leaves 140-146).
Abstracts also in Chinese.
Title from PDF title page (viewed on 14, September, 2016).
Chen, Zhitang.
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Brouillard, Philippe. "Apprentissage de modèles causaux par réseaux de neurones artificiels." Thesis, 2020. http://hdl.handle.net/1866/25096.
Full textAbstract:
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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Pinto, Ana Raquel de Melo. "Using Machine Learning to Measure Democracy and Economic Development, and the Causal Relationship Between the Two." Master's thesis, 2021. https://hdl.handle.net/10216/137617.
Full text
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Lu, Rui. "Feature Selection for High Dimensional Causal Inference." Thesis, 2020. https://doi.org/10.7916/d8-52mk-ft68.
Full textAbstract:
Selecting an appropriate set for confounding control is essential for causal inference. The strong ignorability is a strong assumption. With observational data, researchers are unsure the strong ignorability assumption holds. To reduce the possibility of the bias caused by unmeasured confounders, one solution is to include the widest range of pre-treatment covariates, which has been demonstrated to be problematic. Subjective knowledge-based covariate screening is a common approach that has been applied widely. However, under high dimensional settings, it becomes difficult for domain experts to screen thousands of covariates. Machine learning based automatic causal estimation makes it possible for high dimensional causal estimation. While the theoretical properties of these techniques are desirable, they are only necessarily applicable asymptotically (i.e., requiring large sample sizes to be guaranteed to hold), and their performance in smaller samples is sometimes less clear. Data-based pre-processing approaches may fill this gap. Nevertheless, there is no clear guidance on when and how covariate selection should be involved in high dimensional causal estimation.
In this dissertation, I address the above issues by (a) providing a classification scheme for major causal covariate selections methods (b) extending causal covariate selection framework (c) conducting a comprehensive empirical Monte Carlo simulation study to illustrate theoretical properties of causal covariate selection and estimation methods, and (d) following-up with a case study to compare different covariate selection approaches in a real data testing ground.
Under small sample and/or high dimensional settings, study results indicate choosing an appropriate covariate selection method as pre-processing tool is necessary for causal estimation. Under relatively large sample and low dimensional settings, covariate selection is not necessary for machine learning based automatic causal estimation. Careful pre-processing guided by subjective knowledge is essential.
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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.
Full textAbstract:
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.
Find full textAbstract:
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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"Detecting Frames and Causal Relationships in Climate Change Related Text Databases Based on Semantic Features." Doctoral diss., 2018. http://hdl.handle.net/2286/R.I.49062.
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abstract: The subliminal impact of framing of social, political and environmental issues such as climate change has been studied for decades in political science and communications research. Media framing offers an “interpretative package" for average citizens on how to make sense of climate change and its consequences to their livelihoods, how to deal with its negative impacts, and which mitigation or adaptation policies to support. A line of related work has used bag of words and word-level features to detect frames automatically in text. Such works face limitations since standard keyword based features may not generalize well to accommodate surface variations in text when different keywords are used for similar concepts.
This thesis develops a unique type of textual features that generalize Dissertation/Thesis
Doctoral Dissertation Computer Science 2018
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Bhattacharya, Indranil. "Feature Selection under Multicollinearity & Causal Inference on Time Series." Thesis, 2017. http://etd.iisc.ernet.in/2005/3980.
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In this work, we study and extend algorithms for Sparse Regression and Causal Inference problems. Both the problems are fundamental in the area of Data Science.
The goal of regression problem is to nd out the \best" relationship between an output variable and input variables, given samples of the input and output values. We consider sparse regression under a high-dimensional linear model with strongly correlated variables, situations which cannot be handled well using many existing model selection algorithms. We study the performance of the popular feature selection algorithms such as LASSO, Elastic Net, BoLasso, Clustered Lasso as well as Projected Gradient Descent algorithms under this setting in terms of their running time, stability and consistency in recovering the true support. We also propose a new feature selection algorithm, BoPGD, which cluster the features rst based on their sample correlation and do subsequent sparse estimation using a bootstrapped variant of the projected gradient descent method with projection on the non-convex L0 ball. We attempt to characterize the efficiency and consistency of our algorithm by performing a host of experiments on both synthetic and real world datasets.
Discovering causal relationships, beyond mere correlation, is widely recognized as a fundamental problem. The Causal Inference problems use observations to infer the underlying causal structure of the data generating process. The input to these problems is either a multivariate time series or i.i.d sequences and the output is a Feature Causal Graph where the nodes correspond to the variables and edges capture the direction of causality. For high dimensional datasets, determining the causal relationships becomes a challenging task because of the curse of dimensionality. Graphical modeling of temporal data based on the concept of \Granger Causality" has gained much attention in this context. The blend of Granger methods along with model selection techniques, such as LASSO, enables efficient discovery of a \sparse" sub-set of causal variables in high dimensional settings. However, these temporal causal methods use an input parameter, L, the maximum time lag. This parameter is the maximum gap in time between the occurrence of the output phenomenon and the causal input stimulus. How-ever, in many situations of interest, the maximum time lag is not known, and indeed, finding the range of causal e ects is an important problem. In this work, we propose and evaluate a data-driven and computationally efficient method for Granger causality inference in the Vector Auto Regressive (VAR) model without foreknowledge of the maximum time lag. We present two algorithms Lasso Granger++ and Group Lasso Granger++ which not only constructs the
hypothesis feature causal graph, but also simultaneously estimates a value of maxlag (L) for each variable by balancing the trade-o between \goodness of t" and \model complexity".
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Chuang, Wen-Tze, and 莊文澤. "Detecting Critical Timing Paths Caused by Dynamic Voltage Drop Using Machine Learning." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/j9nhr6.
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碩士國立臺灣大學
資訊工程學研究所
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Timing constrain will reduce operational frequency of large integrated circuits or system-on-a-chip, and it is often caused by setup timing violation which would be influenced by dynamic voltage drop, can be referred to as maximum timing pushout. This problem is exacerbated in the FinFET designs. This thesis proposes a method using machine learning techniques to predict critical scenarios quickly for analyzing dynamic voltage drop and critical timing paths predictor for accurate timing analysis. First, we use a classification model to predict critical level of timing paths, and use a regression model or a ranking model to predict ranking of the critical timing paths afterwards. Next, we can determine the critical scenarios for analyzing dynamic voltage drop. After the analysis, we use the classification model to predict critical level of timing paths which is similar to the first step. In our method, the best classification model can achieve about 90% accuracy, and 80% of hit-rate in Top-5 critical scenarios predicting.
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Olowe, Feranmi Jeremiah. "Spatial prediction of flood susceptible areas using machine learning approach: a focus on west african region." Master's thesis, 2021. http://hdl.handle.net/10362/113893.
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Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesThe constant change in the environment due to increasing urbanization and climate change has led to recurrent flood occurrences with a devastating impact on lives and properties. Therefore, it is essential to identify the factors that drive flood occurrences, and flood locations prone to flooding which can be achieved through the performance of Flood Susceptibility Modelling (FSM) utilizing stand-alone and hybrid machine learning models to attain accurate and sustainable results which can instigate mitigation measures and flood risk control. In this research, novel hybridizations of Index of Entropy (IOE) with Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF) was performed and equally as stand-alone models in Flood Susceptibility Modelling (FSM) and results of each model compared. First, feature selection and multi-collinearity analysis were performed to identify the predictive ability and the inter-relationship among the factors. Subsequently, IOE was performed as bivariate and multivariate statistical analysis to assess the correlation among the flood influencing factor’s classes with flooding and the overall influence (weight) of each factor on flooding. Subsequently, the weight generated was used in training the machine learning models. The performance of the proposed models was assessed using the popular Area Under Curve (AUC) and statistical metrics. Percentagewise, results attained reveals that DT-IOE hybrid model had the highest prediction accuracy of 87.1% while the DT had the lowest prediction performance of 77.0%. Among the other models, the result attained highlight that the proposed hybrid of machine learning and statistical models had a higher performance than the stand-alone models which reflect the detailed assessment performed by the hybrid models. The final susceptibility maps derived revealed that about 21% of the study area are highly prone to flooding and it is revealed that human-induced factors do have a huge influence on flooding in the region.
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Ferreira, Guerra Steve. "Une procédure de sélection automatique de la discrétisation optimale de la ligne du temps pour des méthodes longitudinales d’inférence causale." Thèse, 2017. http://hdl.handle.net/1866/20549.
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Jehangiri, Ali Imran. "Distributed Anomaly Detection and Prevention for Virtual Platforms." Doctoral thesis, 2015. http://hdl.handle.net/11858/00-1735-0000-0022-605F-2.
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Minerve, Mampaka Maluambanzila. "Quadri-dimensional approach for data analytics in mobile networks." Diss., 2018. http://hdl.handle.net/10500/25882.
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The telecommunication market is growing at a very fast pace with the evolution of new technologies to support high speed throughput and the availability of a wide range of services and applications in the mobile networks. This has led to a need for communication service providers (CSPs) to shift their focus from network elements monitoring towards services monitoring and subscribers’ satisfaction by introducing the service quality management (SQM) and the customer experience management (CEM) that require fast responses to reduce the time to find and solve network problems, to ensure efficiency and proactive maintenance, to improve the quality of service (QoS) and the quality of experience (QoE) of the subscribers. While both the SQM and the CEM demand multiple information from different interfaces, managing multiple data sources adds an extra layer of complexity with the collection of data.
While several studies and researches have been conducted for data analytics in mobile networks, most of them did not consider analytics based on the four dimensions involved in the mobile networks environment which are the subscriber, the handset, the service and the network element with multiple interface correlation.
The main objective of this research was to develop mobile network analytics models applied to the 3G packet-switched domain by analysing data from the radio network with the Iub interface and the core network with the Gn interface to provide a fast root cause analysis (RCA) approach considering the four dimensions involved in the mobile networks. This was achieved by using the latest computer engineering advancements which are Big Data platforms and data mining techniques through machine learning algorithms.Electrical and Mining Engineering
M. Tech. (Electrical Engineering)