时   间：2023年12月27日（周三）15:30-17:00

地   点：思源楼326室

题   目：Assessing Heterogeneity of Treatment Effects

主讲人：Jianfei Cao, Assistant Professor of Economics, Northeastern University

主持人：俞学文 青年副研究员

摘   要：

Treatment effect heterogeneity is of major interest in economics, but its assessment is often hindered by the fundamental lack of identification of the individual treatment effects. For example, we may want to assess the effect of insurance on the health of otherwise unhealthy individuals, but it is infeasible to insure only the unhealthy, and thus the causal effects for those are not identified. Or, we may be interested in the shares of winners from a minimum wage increase, while without observing the counterfactual, the winners are not identified. Such heterogeneity is often assessed by quantile treatment effects, which do not come with clear interpretation and the takeaway can sometimes be equivocal. We show that, with the quantiles of the treated and control outcomes, the ranges of these quantities are identified and can be informative even when the average treatment effects are not significant. Two applications illustrate how these ranges can inform us about heterogeneity of the treatment effects.

个人简介：

Jianfei Cao is an assistant Professor of Economics at Northeastern University. Prior to this, he studied at the University of Chicago Booth School of Business for his PhD in Econometrics and Statistics. He studies applied and theoretical econometrics. His research has chiefly been in the areas of modern statistical methods in economic applications, causal inference in comparative case studies, and weak identification. His most recent research has studied the properties of clustering structures learned from the data, in problems involving estimation and inference of causal effects.