Difference-in-differences for mediation analysis using double machine learning

arXiv 27 Feb 2026 · Econometrics

arXiv:2602.23877 · PDF · DOI · OpenAlex · Extracted main text

Abstract

We propose a difference-in-differences (DiD) framework with mediation for possibly multivalued discrete or continuous treatments and mediators, aimed at identifying the direct effect of the treatment on the outcome (net of effects operating through the mediator), the indirect effect via the mediator, and the joint effects of treatment and mediator, consistent with the framework of dynamic treatment effects. Identification relies on a conditional parallel trends assumption imposed on the mean potential outcome across treatment and mediator states, or (depending on the causal parameter) additionally on the mean potential outcomes and potential mediator distributions across treatment states. We propose ATET estimators for repeated cross sections and panel data within the double/debiased machine learning framework, which allows for data-driven control of covariates, and we establish their asymptotic normality under standard regularity conditions. We investigate the finite-sample performance of the proposed methods in a simulation study and illustrate our approach in an empirical application to the US National Longitudinal Survey of Youth, estimating the direct effect of health care coverage on general health as well as the indirect effect operating through routine checkups.

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Most heavily cited references

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	J Pearl (2001) Direct and indirect effects	0.928	4	3	100%
2	Farbmacher, Helmut and Huber, Martin and Lafférs, Luká s and Langen,… (2022) Causal mediation analysis with double machine learning self	0.874	16	2	100%
3	Schenk, Timo Daniel (2024) Mediation analysis in difference-in-differences designs	0.811	4	2	100%
4	Deuchert, E and Huber, M and Schelker, M (2019) Direct and indirect effects based on difference-in-differences with an application to political preferences following the Vietna… self	0.737	3	2	100%
5	J M Robins and Sander Greenland (1992) Identifiability and Exchangeability for Direct and Indirect Effects	0.737	3	2	100%
6	Alexandre Belloni and Victor Chernozhukov and Christian Hansen (2014) Inference on Treatment Effects after Selection among High-Dimensional Controls	0.644	2	2	100%
7	Goodman-Bacon, A (2021) Difference-in-differences with variation in treatment timing	0.644	2	2	100%
8	J Neyman (1959) Optimal asymptotic tests of composite statistical hypotheses	0.644	2	2	100%
9	J M Robins (1986) A new approach to causal inference in mortality studies with sustained exposure periods - application to control of the healthy…	0.644	2	2	100%
10	J M Robins and M A Hernan and B Brumback (2000) Marginal Structural Models and Causal Inference in Epidemiology	0.644	2	2	100%

Showing the top 10 of 52 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

	Citing paper	Intensity	Mentions	Sections
1	Difference-in-Differences with a Mediator	0.644	2	2
2	2606.24785	0.405	1	1