Double/Debiased Machine Learning for Continuous Treatment Effects in Panel Data with Endogeneity
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866909053933846528 |
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| author | Wu, Peikai Sun, Kuan Xiao, Zhiguo |
| author_facet | Wu, Peikai Sun, Kuan Xiao, Zhiguo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_17910 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Double/Debiased Machine Learning for Continuous Treatment Effects in Panel Data with Endogeneity Wu, Peikai Sun, Kuan Xiao, Zhiguo Methodology 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. |
| title | Double/Debiased Machine Learning for Continuous Treatment Effects in Panel Data with Endogeneity |
| topic | Methodology |
| url | https://arxiv.org/abs/2605.17910 |