Paul S. Clarke, Annalivia Polselli
arXiv 13 Dec 2023 · Econometrics · publishedEconometrics Journal (2025) · 11 citations (OpenAlex)
arXiv:2312.08174 · PDF · DOI · OpenAlex · Extracted main text
Recent advances in causal inference have seen the development of methods which make use of the predictive power of machine learning algorithms. In this paper, we develop novel double machine learning (DML) procedures for panel data in which these algorithms are used to approximate high-dimensional and nonlinear nuisance functions of the covariates. Our new procedures are extensions of the well-known correlated random effects, within-group and first-difference estimators from linear to nonlinear panel models, specifically, Robinson (1988)'s partially linear regression model with fixed effects and unspecified nonlinear confounding. Our simulation study assesses the performance of these procedures using different machine learning algorithms. We use our procedures to re-estimate the impact of minimum wage on voting behaviour in the UK. From our results, we recommend the use of first-differencing because it imposes the fewest constraints on the distribution of the fixed effects, and an ensemble learning strategy to ensure optimum estimator accuracy.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Semenova, V., M. Goldman, V. Chernozhukov, and M. Taddy (2023) Inference on heterogeneous treatment effects in high-dimensional dynamic panels under weak dependence | 1.000 | 11 | 4 | 100% |
| 2 | Klosin, S. and M. Vilgalys (2023) Estimating continuous treatment effects in panel data using machine learning with an agricultural application | 1.000 | 9 | 3 | 100% |
| 3 | Robinson, P. M (1988) Root-n-consistent semiparametric regression | 0.928 | 4 | 3 | 100% |
| 4 | Chernozhukov, V., D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W… (2018) Double/debiased machine learning for treatment and structural parameters | 0.874 | 15 | 6 | 67% |
| 5 | Wooldridge, J. M (2019) Correlated random effects models with unbalanced panels | 0.874 | 6 | 2 | 100% |
| 6 | Chang, N.-C (2020) Double/debiased machine learning for difference-in-differences models | 0.874 | 5 | 2 | 100% |
| 7 | Fazio, A. and T. Reggiani (2023) Minimum wage and tolerance for high incomes | 0.811 | 4 | 2 | 100% |
| 8 | Wooldridge, J. M. and Y. Zhu (2020) Inference in approximately sparse correlated random effects probit models with panel data | 0.811 | 4 | 2 | 100% |
| 9 | Athey, S. and G. Imbens (2016) Recursive partitioning for heterogeneous causal effects | 0.644 | 2 | 2 | 100% |
| 10 | Athey, S., J. Tibshirani, and S. Wager (2019) Generalized random forests | 0.644 | 2 | 2 | 100% |
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