Before revision: 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.
After revision: 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 covariates that admits an unknown, possibly nonlinear factor structure is exploited to control for the latent confounders.