Abstract: We propose an instrumental variable framework for identifying and estimating average and quantile effects of discrete and continuous treatments with binary instruments. The basis of our approach is a local copula representation of the joint distribution of the potential outcomes and unobservables determining treatment assignment. This representation allows us to introduce an identifying assumption, so-called copula invariance, that restricts the local dependence of the copula with respect to the treatment propensity. We show that copula invariance identifies treatment effects for the entire population and other subpopulations such as the treated. The identification results are constructive and lead to straightforward semiparametric estimation procedures based on distribution regression. An application to the effect of sleep on well-being uncovers interesting patterns of heterogeneity.

Bio: Sukjin Han is a Professor of Economics at the University of Bristol, United Kingdom. As an econometrician, he specializes in causal inference, covering topics ranging from the identification and estimation of treatment and policy effects in various nonparametric models with endogeneity, to optimal treatment allocation problems in observational settings, and nonstandard inference problems in weak and partial identification. Another focus of his research is the development of empirical frameworks for using visual and text data in economic analyses by applying machine learning methods. Before moving to Bristol, he was an assistant professor of Economics at the University of Texas at Austin. He received his Ph.D. in Economics from Yale University in 2012.