Modern methods for causal inference and missing data

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.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2025

Author dc:creator

• Xia, Eric

Advisor dc:contributor.advisor

• Wainwright, Martin J.

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/164650

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/164650