Michel F. C. Haddad, Martin Huber, Lucas Z. Zhang
arXiv 28 Oct 2024 · Econometrics
arXiv:2410.21105 · PDF · DOI · OpenAlex · Extracted main text
We propose a difference-in-differences (DiD) method for a time-varying continuous treatment and multiple time periods. Our framework assesses the average treatment effect on the treated (ATET) when comparing two non-zero treatment doses. The identification is based on a conditional parallel trend assumption imposed on the mean potential outcome under the lower dose, given observed covariates and past treatment histories. We employ kernel-based ATET estimators for repeated cross-sections and panel data adopting the double/debiased machine learning framework to control for covariates and past treatment histories in a data-adaptive manner. We also demonstrate the asymptotic normality of our estimation approach under specific regularity conditions. In a simulation study, we find a compelling finite sample performance of undersmoothed versions of our estimators in setups with several thousand observations.
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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 | Zhang, Lucas Z (2025) Continuous difference-in-differences with double/debiased machine learning self | 0.941 | 12 | 5 | 83% |
| 2 | Chernozhukov, Victor and Chetverikov, Denis and Demirer, Mert and Du… (2018) Double/debiased machine learning for treatment and structural parameters | 0.928 | 5 | 4 | 80% |
| 3 | de Chaisemartin, Clément and d'Haultfoeuille, Xavier and Pasquier, F… (2024) Difference-in-Differences for Continuous Treatments and Instruments with Stayers | 0.874 | 5 | 2 | 100% |
| 4 | Hugo Bodory and Martin Huber (2018) The causalweight package for causal inference in R self | 0.737 | 3 | 2 | 100% |
| 5 | Fricke, Hans (2017) Identification Based on Difference-in-Differences Approaches with Multiple Treatments | 0.644 | 2 | 2 | 100% |
| 6 | Michael Zimmert (2020) Efficient Difference-in-Differences Estimation with High-Dimensional Common Trend Confounding | 0.644 | 2 | 2 | 100% |
| 7 | Chang, Neng-Chieh (2020) Double/debiased machine learning for difference-in-differences models | 0.511 | 2 | 2 | 50% |
| 8 | Chernozhukov, V. and Chetverikov, D. and Kato, K (2014) Gaussian approximation of suprema of empirical processes | 0.511 | 2 | 1 | 100% |
| 9 | Chernozhukov, V. and Chetverikov, D. and Kato, K (2014) Anti-concentration and honest, adaptive confidence bands | 0.511 | 2 | 1 | 100% |
| 10 | Fan, Q. and Hsu, Y. C.and Lieli, R. P. and Zhang, Y (2022) Estimation of conditional average treatment effects with high-dimensional data | 0.511 | 2 | 1 | 100% |
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