New developments in causal inference using balance optimization subset selection

Causal inference with observational data has drawn attention across various fields. These observational studies typically use matching methods which find matched pairs with similar covariate values. However, matching methods may not directly achieve covariate balance, a measure of matching effectiveness. As an alternative, the Balance Optimization Subset Selection (BOSS) framework, which seeks the optimal covariate balance directly, has been proposed. This dissertation extends the BOSS framework in various ways and is composed of the following five parts. The first part of the dissertation investigates all the possible cases that may lead to bias in the context of BOSS and tries to mitigate the bias. Second, this dissertation then extends the BOSS by estimating and decomposing a treatment effect as a combination of heterogeneous treatment effects from a partitioned set using the BOSS. Third, the dissertation generalizes the BOSS framework from a binary treatment setting to a multi-treatment setting. A treatment effect estimate with multiple treatments can be computed by combining estimates obtained from BOSS with binary treatments. The fourth part discusses on how to handle missing data with BOSS. It includes a sensitivity analysis of BOSS studying how the estimated values are affected by violation of the conditional independence assumption and methods to apply BOSS after multiple imputation on missing covariates. In these discussions, the performances of BOSS estimators are compared to those of matching estimators. In the last part, BOSS is formulated as an LP by relaxing integer constraints in the original mixed integer programming formulation and properties of its dual problem are investigated.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Systems & Entrepreneurial Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2018

Author dc:creator

• Kwon, Hee Youn

Contributors dc:contributor

• Jacobson, Sheldon H.
• He, Niao
• Chandrasekaran, Karthekeyan
• Nagi, Rakesh

• Causal Analysis
• Optimization
• Subset Selection

Statement dc:rights

• Copyright 2018 Hee Youn Kwon

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/100968

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/100968