SMU SOE Seminar Series (November 1, 2024): Causal Inference in Possibly Nonlinear Factor Models

TOPIC:

ABSTRACT

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-robusts core 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.

Keywords: Causal Inference, Latent Confounders, Noisy Measurements, Nonlinear Factor Model, Heterogeneous Treatment Effects, Doubly-Robust Estimator, High-Dimensional Data.

JEL: C14, C21, C23, C38.

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