Department of Industrial Engineering & Decision Analytics [Joint IEDA/ISOM seminar]  - Estimating Causal Effects of Discrete and Continuous Treatments with Binary Instruments

10:30am - 11:30am
Room 4502 (lift 25-26)

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.

讲者/ 表演者:
Prof. Iván Fernández-Val
Boston University, Department of Economics

Iván Fernández-Val is a Professor at the Department of Economics in Boston University. His research fields are Econometrics and Labor Economics. He has recently worked on nonlinear panel data, distributional and causal methods, and applications of machine learning to causal inference. His work has been published in top economic and statistics journals such as Biometrika, Econometrica, Journal of the American Statistical Association, Journal of Econometrics, Journal of Machine Learning Research, Journal of Political Economy and  Review of Economic Studies. He serves as coeditor and associate editor of several journals.

语言
英文
适合对象
教职员
研究生
主办单位
Department of Industrial Engineering & Decision Analytics
信息,商业统计及营运学系
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