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

Lucas Z. Zhang

Papers from arXiv.org

Abstract: This paper extends difference-in-differences 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.

Date: 2024-08, Revised 2025-12
New Economics Papers: this item is included in nep-big and nep-ecm
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Published in The Econometrics Journal, 2025

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2408.10509

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