Modern methods for causal inference and missing data

Abstract

The proliferation of data-driven approaches in a wide array of settings is one of the defining characteristic of the modern era. With this rise, there 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 practitioners in a variety of disciplines, including policy-makers looking to make critical decisions that can influence lives of many. On the surface these problems seem quite distinct, yet the literature has highlighted deep connections between these two settings. Indeed, many methods for addressing one question can often be repurposed to address the other. These two settings are quite classical and approaches to address the are still quite so, but there has been great interest recently to develop techniques and algorithms to address them that harness modern developments in statistics and machine learning. This thesis contributes to the literature by providing new methods as well as novel understandings of existing ones.