Double/Debiased Machine Learning for Continuous Treatment Effects in Panel Data with Endogeneity
摘要
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.
引用
@article{arxiv.2605.17910,
title = {Double/Debiased Machine Learning for Continuous Treatment Effects in Panel Data with Endogeneity},
author = {Peikai Wu and Kuan Sun and Zhiguo Xiao},
journal= {arXiv preprint arXiv:2605.17910},
year = {2026}
}