Causal inference methods are powerful techniques which allow the practitioner to estimate treatment effects while simultaneously injecting cause and effect into the problem at hand. However, when causal estimation procedures ...

This thesis considers the design of Markov chain Monte Carlo (MCMC) estimators using couplings. Couplings play an important part in Markov chain theory, and in recent decades they have also taken on a central role in ...

In this dissertation, we aim to adapt reinforcement learning (RL) to real-world, high-risk settings. We study how to optimize sequential decision-making in complex settings with large observational data repositories where ...

This dissertation includes 4 chapters on methods for health policy research. In the first 3 chapters, we consider common approaches to observational policy analyses. We discuss statistical and practical problems with the ...

Studies are often performed in samples that do not resemble the target populations relevant for policy, treatment, or other decisions. Much of the causal inference literature has focused on addressing internal validity ...

This dissertation is composed of three essays in financial economics. Each essay documents a set of different empirical asset pricing facts and proposes a theoretical explanation. The first addresses the persistence of ...

This dissertation develops statistical theories and methodologies in the realm of false discovery rate (FDR) control and factor analysis. Both these topics are of great scientific importance in the field of social science, ...

Battling the curse of dimensionality is a common theme in modern statistics. This dissertation contributes to this effort by surveying theory and methods in high-dimensinonal statistics developed by the author and ...

People share resources with each other across meaningful social relationships. It has been demonstrated that increased resources cause better health for individuals, yet there is no epidemiologic research investigating the ...

A common model used for analyzing paired comparison data is the Bradley-Terry model. An implicit assumption embedded within the Bradley-Terry framework is stochastic transitivity. This implies that if A is preferred to B, ...