Causal Inference in Possibly Nonlinear Factor Models
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.
Bio: Yingjie Feng is a postdoctoral research associate in the Department of Politics 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.