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.
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Showing 1 to 20 of 215 for “"Causal inference."”.
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Topics in conditional causal inference
… designs and large-scale observational data, causal questions arising in applications are now more targeted and precise. For example, one might ask if the treatment is effective at a particular time point, or if the treatment is effective for a particular individual. To answer many questions …
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Causal Inference Under Privacy Constraints
Causal inference is an important tool for learning the effects of interventions in observational or experimental settings. It is widely used in many fields such as epidemiology, economics, and political science to find answers like the average treatment effect of a medical procedure or the …
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Data-Rich Personalized Causal Inference
There is a growing interest in individual-level causal questions to enable personalized decision-making. For example, what happens to a particular patient’s health if we prescribe a drug to them, or what happens to a particular consumer’s behavior if we recommend a product to them? Conducting …
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Causal Inference Methods for Microbiome Data
… methodological frameworks for advancing causal mediation analysis in microbiome research. Microbiome data are characterized by high dimensionality, sparsity, overdispersion, and strong interdependencies, making conventional mediation approaches ill-suited for reliable inference. Existing …
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Applications of Methods in Causal Inference
<p>Causal inference methods in economics serve as vital tools for disentangling cause-and-effect relationships. These methods enable economists to find the impact of policy interventions, and market changes on desired outcomes. Employing techniques such as randomized controlled trials, instrumental …
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Algorithmic Approaches to Nonparametric Causal Inference
This thesis presents procedures for performing inferences of causal parameters across an array of contexts including observational studies, completely randomized designs, paired experiments, and covariate-adaptive designs. First, we discuss an application of convex optimization to conduct …
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Causal Inference Beyond Estimating Average Treatment Effects
… questions are to understand and reveal the causal mechanisms from observational study data or experimental data. Over the past several decades, there has been a large number of developments to render causal inferences from observed data. Most developments are designed to estimate the mean …
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CAUSAL INFERENCE METHODS FOR ELECTRICAL CONSUMPTION’S ESTIMATION
… research. The study delves into the realm of causal inference, a crucial statistical tool for understanding the impacts of interventions in situations where controlled experiments are not feasible. It particularly addresses the challenge of evaluating the real effect of thermal renovations on …
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Selected topics on design-based causal inference
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-08-01
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Causal Inference for Social and Engineering Systems
… The key framework we introduce is connecting causal inference with tensor completion. In particular, we represent the various potential outcomes (i.e., counterfactuals) of interest through an order-3 tensor. The key theoretical results presented are: (i) Formal identification results …
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Three Essays on Causal Inference With Model Averaging
<p>This dissertation contains essays on causal inference with model averaging. The first essay presents a theoretical derivation of a model-averaging-based average treatment effect estimator. The second essay provides comparison of predictability of treated counterfactual outcome between model …
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Machine Learning for causal Inference on Observational Data
… although RCTs are the best way to determine causal effects, the chances to perform such rigorous scientific experiments is, most often, either impossible or unethical. The Average Treatment Effect (ATE) is usually the outcome of the RCT experiments and this outcome is ideally proof of an …
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DoViz: Intervention-centric interactive visualization for causal inference
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-05-01
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Causal Inference: Heterogeneous Effects and Non-stationary Environments
… effects can be understood through the lens of causal inference and the estimation of treatment effects. First, we use the experiment data to build causal models that aim to maximize the probability of purchase by customizing the number of options shown to each customer. We show that even when …
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Essays on Econometrics, Causal Inference, and Machine Learning
… this dissertation, I develop tools for flexible causal inference, weaving machine learning into econometrics and solving unique problems that arise at their intersection. Specifically, I work in three domains at the intersection between econometrics and machine learning: (Chapter 1) causal …
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Modern methods for causal inference and missing data
… has been much focus on using data to answer causal questions, e.g. whether A causes a change in B. Furthermore aspects of data collection has given rise to datasets that are often quite messy, sometimes missing important entries. These are both problems that are incredibly relevant to …
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Representation Learning Based Causal Inference in Observational Studies
… investigates novel statistical approaches for causal effect estimation in observational settings, where controlled experimentation is infeasible and confounding is the main hurdle in estimating causal effect. As such, deconfounding constructs the main subject of this dissertation, that is (i) …
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Causal Inference: A New Name for an Old Concept?
Studies in perceptual psychology and neuroscience have investigated multisensory integration and have come to differing conclusions regarding the mechanisms behind the phenomenon. Though this stark difference exists, many of the rules regarding cue presentation, development and beneficial outcomes …
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