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

Peikai Wu; Kuan Sun; Zhiguo Xiao

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arXiv · 2605.17910

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

Peikai Wu·Kuan Sun·Zhiguo Xiao

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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.

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BibTeX RIS

Peikai Wu, Kuan Sun, Zhiguo Xiao. 2026-05-18. Double/Debiased Machine Learning for Continuous Treatment Effects in Panel Data with Endogeneity. https://arxiv.org/abs/2605.17910

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