Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 16 of 16 for “"Unmeasured Confounding"”.
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Topics in offline statistical reinforcement learning: addressing challenges in continuous actions, distribution shifts, and unmeasured confounding
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-08-01
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MATERNAL USE OF PSYCHIATRIC MEDICATIONS DURING PREGNANCY AND ADVERSE BIRTH OUTCOMES AND NEURODEVELOPMENTAL PROBLEMS IN OFFSPRING
… among exposed and unexposed pregnancies (i.e., confounding factors). Therefore, the aim of my dissertation research was to evaluate consequences of prenatal exposure to psychiatric and analgesic medications on risk for adverse birth outcomes and neurodevelopmental problems by seeking converging …
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Empowering RCT with Multi-site Multi-source RWD: a Statistical Learning Perspective
… are recognized for their ability to minimize confounding through randomization, thereby establishing robust causal inferences in clinical research. However, the stringent protocols and limited sample sizes typical of RCTs can restrict statistical efficiency and the scope of inference. In …
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Bayesian models for unmeasured confounder in the analysis of time-to-event data.
… approaches with distributional assumptions of an unmeasured confounding variable, and then we compare them with the naive model. This assumes we ignore the effect of the unmeasured confounder. To explore the ability of bias adjustment from different sources of information, we offer a Bayesian …
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Some Selective Inference and Optimization Methods for Reliable Causal Inference
… experimental data and observational data with unmeasured confounding. It is organized in three chapters. The first chapter outlines the historical development of causal inference and reviews different frameworks for defining causality. We describe common approaches to draw inference from both …
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Network meta-analysis with rare events and misclassified response.
… as misclassification, measurement error, and unmeasured confounding can lead to substantially biased estimators. It is strongly recommended that epidemiologists not only acknowledge these sorts of errors in data but also incorporate sensitivity analyses into part of the total data analysis. In …
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Preoperative Internal Medicine Consultation for Elective Intermediate-to-high Risk Noncardiac Surgery in Ontario
… as well as sensitivity analyses that tested for unmeasured confounding. Third, temporal trends and practice variation in consultation were evaluated within the population-based cohort. The proportion of patients undergoing consultation remained relatively stable over the study period, at …
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Mediation analysis for different types of Causal questions: Effect of Cause and Cause of Effect
… explained as a consequence of the presence of unmeasured confounding between the mediator and the outcome. In this thesis we discuss these apparent paradoxical results in a real dataset. In addition we suggest useful graphical sensitivity analysis techniques to explain the potential amount of …
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Towards More Accurate Causal Inference with Instrumental Variables and Mendelian Randomisation Analyses
… to make causal inference in the presence of unmeasured confounding, and the use of genetic variants as instrumental variables in epidemiological studies is known as Mendelian Randomisation (MR). The conventional modelling assumption for using IVs is to assume a linear structural equation …
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Novel Statistical Methods for Mediation Analysis with High-dimensional Omics Mediators
… inefficiency, inter-study heterogeneity, and unmeasured confounding. This dissertation addresses these challenges through three methodological innovations designed to advance high-dimensional mediation analysis for omics mediators. First, a computationally efficient two-stage framework using …
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Essays on the Effects of Immigration on Labor Markets
… variables and synthetic controls to address unmeasured confounding. We derive conditions under which SIV is consistent and asymptotically normal, even when the standard IV estimator is not. Motivated by the finite sample properties of our estimator, we introduce an ensemble estimator that …
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Robust sensitivity analysis for quantiles of hidden biases and treatment effects in matched observational studies
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-12-01
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From Theory to Practice: Improving Causal Conclusions from Healthcare Data
… adjustment, proximal inference for unobserved confounding, and applications of modern estimation techniques to healthcare-relevant settings. In Chapter 2, we investigate the performance and robustness of state-of-the-art machine learning estimators for causal inference when covariate selection …
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Evaluating South African policies for linkage to and retention in HIV care using quasi-experimental methods
… relative to multiple pills, controlling for unmeasured confounding. In study 3, we used stratified instrumental variable analysis to examine whether the effect of FDCs on attrition varied across subsets of the patient population in the same Johannesburg clinic we evaluated in study 2. We saw …
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Essays on Econometrics and Policy Evaluation
… variables and synthetic controls to address unmeasured confounding. We derive conditions under which SIV is consistent and asymptotically normal, even when the standard IV estimator is not. Motivated by the finite sample properties of our estimator, we introduce an ensemble estimator that …