Title: Review of Treatment Effect Models and Some New Results

Speaker: Yu-Chin Hsu, Academia Sinica

Time: March 24th 2017, 13:30–16:30

Venue: Conference Room 106B, Zhonghui Building (College of Economics, JNU)

About the speaker:

Yu-Chin Hsu is an Associate Research Fellow at the Institute of Economics, Academia Sinica. He received his PhD degree from the Department of Economics, University of Texas at Austin in 2010. His research focuses on econometrics.

Abstract:

Many empirical studies in economics and other social sciences are interested in the causal effects of programs or policies. The main problem studied in this literature is to evaluate the effect of the exposure of a set of units to a program, or treatment, on some outcome. In economic studies, usually the units are individuals, households, markets, firms, counties, states, or countries and the treatments can be job training program, educational programs, vouchers, laws and government policies. For example, we are interested in the effect of a job training program on individual’s income, hourly wage or employment status. For another example, we are also interested in the effect of attending colleges on individual’s future income.

In the last two decades, much research has been done on the econometric and statistical analysis of such causal effects. This course is served as a review on recent development with a focus on the Rubin Causal Model. We review various treatment effects under the assumption that the treatment assignment is unconfounded which is also known as selection-on-observables and conditional independence. Then we extend the results to cases where the treatment assignment is endogenous, but a valid binary instrument is available for the treatment assignment. We also discuss several tests for the unconfoundedness assumption. Last, we summarize some of my research in this area.