To make causal inferences from observational data, researchers have often turned to matching methods. These methods are variably successful. We address issues with matching methods by redefining the matching problem as a subset selection problem. Given a set of covariates, we seek to find two subsets, a control group and a treatment group, so that we obtain optimal balance, or, in other words, the minimum discrepancy between the distributions of these covariates in the control and treatment groups. Our formulation captures the key elements of the Rubin causal model and translates nicely into a discrete optimization framework.
Bio:
Wendy K. Tam Cho is Professor in the Departments of Political Science, Statistics, Mathematics, Computer Science, Asian American Studies, and the College of Law, Senior Research Scientist at the National Center for Supercomputing Applications, Faculty in the Illinois Informatics Institute, and Affiliate of the Cline Center for Advanced Social Research, the Computational Science and Engineering Program, and the Program on Law, Behavior, and Social Science, at the University of Illinois at Urbana-Champaign. She has also been awarded fellowships by the John Simon Guggenheim Memorial Foundation, the Society for Political Methodology, and the Hoover Institution as well as the Center for Advanced Study in the Behavioral Sciences at Stanford University.