Simple Specification-Free Causal Inference in Large Panels

Ben Deaner, Chen-Wei Hsiang and Andrei Zeleneev

Papers from arXiv.org

Abstract: We propose a simple and transparent approach to causal inference using panel data with a large number of units and periods. Instead of imposing a particular parametric specification on the untreated potential outcomes, we assume they follow a nonparametric nonseparable factor model. We use long pre-treatment outcome histories to construct a measure of similarity between the latent factors of different units. Using this measure, we impute missing counterfactual means and propensity scores through kernel smoothing. Under weak smoothness conditions, these estimates can attain the optimal nonparametric convergence rate up to logarithmic factors, provided that the pre-treatment history grows sufficiently quickly with the sample size. We construct a doubly robust estimator of the period-specific average treatment effect on the treated (ATT) and provide conditions under which it is $\sqrt{N}$-consistent and asymptotically normal, centered at the true ATT. Simulations demonstrate accurate inference for a wide range of linear and nonlinear data-generating processes.

Date: 2025-03, Revised 2026-09
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