Causal Inference in Possibly Nonlinear Factor Models
The research develops a causal inference framework for treatment effect models where confounding variables are measured noisily via an unknown, potentially nonlinear factor structure. The method integrates latent variable extraction with local principal subspace approximation to estimate average treatment effects and other causal parameters robustly.