Causal Inference in Possibly Nonlinear Factor Models

时间：2020年11月27日（周五）10：00-11：30 am

地点：北京大学经济学院107教室

主持人： （国发院）沈艳、黄卓、孙振庭、张俊妮、胡博

              （经济学院）王一鸣、王熙、刘蕴霆

主讲人：冯颖杰

报告摘要：

This paper develops a general causal inference method for treatment effects models under selection on unobservables. A large set of covariates that admits an unknown, possibly nonlinear factor structure is exploited to control for the latent confounders. The key building block is a local principal subspace approximation procedure that combines K-nearest neighbors matching and principal component analysis. Estimators of many causal parameters, including average treatment effects and counterfactual distributions, are constructed based on doubly-robust score functions. Large-sample properties of these estimators are established, which only require relatively mild conditions on the principal subspace approximation. The results are illustrated with an empirical application studying the effect of political connections on stock returns of financial firms, and a Monte Carlo experiment. The main technical and methodological results regarding the general local principal subspace approximation method may be of independent interest.

主讲人简介：

冯颖杰：普林斯顿大学博士后研究助理，擅长社会科学中的理论和应用计量经济学、数理统计和定量方法，尤其擅长丰富数据中的因果推断。

颖杰于2019年在密歇根大学获得经济学博士和统计学硕士学位，还于2014年在北京大学获得经济学硕士学位，于2011年获得经济学学士学位。

Yingjie Feng is a postdoctoral research associate at Princeton University. He specializes in theoretical and applied econometrics, mathematical statistics and quantitative methods in social sciences, with particular interest in causal inference in data-rich environments.

Yingjie received his Ph.D. in Economics and M.A. in Statistics in 2019 from the University of Michigan. He also completed an M.A. in Economics in 2014 and a B.A. in Economics in 2011 at Peking University.