This paper develops a general causal inference method for treatment effects models with noisily measured confounders. The key feature is that a large set of noisy measurements are linked with the underlying latent confounders through an unknown, possibly nonlinear factor structure. The main 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 informativeness of noisy measurements and local 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.

Bio:  Yingjie Feng is an associate professor at the School of Economics and Management, Tsinghua University. He specializes in econometrics, mathematical statistics and quantitative methods in social sciences, with particular interest in nonparametric analysis and causal inference. Most of his work is motivated by empirical problems in applied economics and policy evaluation. His research has been published in leading journals such as American Economic Review, Journal of the American Statistical Association, and Annals of Statistics. 

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. Before joining Tsinghua University, he was a postdoctoral research associate at Princeton University.