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Continuous difference-in-differences with double/debiased machine learning

Lucas Zheng Zhang

Econometrics Journal2025https://doi.org/10.1093/ectj/utaf024article
AJG 3ABDC A*
Weight
0.50

Abstract

SUMMARY This paper extends difference-in-differences (DiD) to settings with continuous treatments. Specifically, the average treatment effect on the treated (ATT) at any level of treatment intensity is identified under a conditional parallel trends assumption. Estimating the ATT in this framework requires first estimating infinite-dimensional nuisance parameters, particularly the conditional density of the continuous treatment, which can introduce substantial bias. To address this challenge, we propose estimators for the causal parameters under the double/debiased machine learning framework and establish their asymptotic normality. Additionally, we provide consistent variance estimators and construct uniform confidence bands based on a multiplier bootstrap procedure. To demonstrate the effectiveness of our approach, we revisit a previous study on the 1983 Medicare Prospective Payment System reform, reframing it as a DiD with continuous treatment and non-parametrically estimating its effects.

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https://doi.org/https://doi.org/10.1093/ectj/utaf024

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@article{lucas2025,
  title        = {{Continuous difference-in-differences with double/debiased machine learning}},
  author       = {Lucas Zheng Zhang},
  journal      = {Econometrics Journal},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1093/ectj/utaf024},
}

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0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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