Speaker

Dr. Soojin Park
Graduate School of Education
University of California, Riverside

Dr. Soojin Park is an Assistant Professor of quantitative methods in the Graduate School of Education at the University of California, Riverside. She received her Ph.D. in education from the University of Wisconsin-Madison in 2016. Before joining the Ph.D. program, she worked as a junior educational policy analyst for Programme for International Studies Assessment (PISA) at the Organisation for Economic Cooperation and Development (OECD).

Title

Estimation and Sensitivity Analysis for Causal Decomposition: Assessing Robustness Toward Omitted Variable Bias

Abstract

A key objective of decomposition analysis is to identify risks or resources (‘mediators’) that contribute to disparities between groups of individuals defined by social characteristics such as race, ethnicity, gender, class, and sexual orientations. In decomposition analysis, a scholarly interest often centers on estimating how much the disparity (e.g., health disparities between Black women and White men) would be reduced/remain if we set the mediator (e.g., education) distribution of one social group equal to another. However, causally identifying disparity reduction and remaining depends on the no omitted mediator-outcome confounding assumption, which is not empirically testable. In this talk, we discuss a flexible way to 1) estimate disparity reduction and remaining and 2) assess the robustness of the estimates to the possible violation of no omitted mediator-outcome confounding. We apply the proposed methods to an empirical example, examining the contribution of education in reducing health disparities across race-gender groups. Our proposed methods are available as open-source software (‘causal.decomp’ R package).