Double/Debiased Machine Learning for Continuous Treatment Effects in Panel Data with Endogeneity

TL;DR

This paper introduces a novel double/debiased machine learning approach for estimating continuous treatment effects in panel data with endogeneity, extending IV methods and ensuring valid inference.

Contribution

It develops a new framework combining machine learning, instrumental variables, and cross-fitting for consistent estimation of treatment effects in complex panel models.

Findings

Estimators are consistent and asymptotically normal.

Simulation results show reduced bias and accurate confidence intervals.

Application uncovers dynamic effects of family SES on childhood BMI.

Abstract

We propose a double/debiased machine learning framework to estimate average derivative effects in nonparametric panel models with two-way fixed effects. It extends instrumental variable methods to panel settings, handles continuous treatments and various forms of endogeneity, and introduces a cross-fitting scheme to restore independence after eliminating time fixed effects. A penalized GMM debiasing term enables automatic debiased machine learning with endogeneity. Our estimators for contemporaneous, dynamic, and aggregated effects are consistent and asymptotically normal with a valid variance estimator. Simulations show reduced regularization bias and accurate confidence intervals. An application to ECLS-K data reveals rich dynamics in the effect of family SES on childhood BMI.