Yuhao Deng, Haoyu Wei, Zhongzhe Ouyang
arXiv 27 Apr 2026 · Econometrics
arXiv:2604.24049 · PDF · DOI · OpenAlex · Extracted main text
Causal mediation analysis is a powerful tool for disentangling the total effect of a treatment into its direct effect on the outcome and its indirect effect mediated through an intermediate variable. However, in observational studies, confounding between treatment and potential outcomes typically renders the total and natural effects non-identifiable. In this work, we advance mediation analysis within the difference-in-differences framework. Under a mediator-adjusted parallel trends assumption and additional conditions, we demonstrate that natural indirect, direct, and total effects are identifiable in the treated group. We further derive efficient influence functions for these estimands, enabling the construction of multiply robust and nonparametrically efficient estimators. We establish the asymptotic properties of these estimators. Applying our methodology to data from the Job Corps Study, we find that job training significantly increases both short-term and long-term earnings, after controlling for the indirect effect through the proportion of weeks employed.
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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 | Baron, Reuben M and Kenny, David A (1986) The moderator–mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerati… | 0.644 | 2 | 2 | 100% |
| 2 | Blackwell, Matthew and Glynn, Adam N and Hilbig, Hanno and Phillips,… (2025) Estimating controlled direct effects with panel data: an application to reducing support for discriminatory policies | 0.644 | 2 | 2 | 100% |
| 3 | Hsia, Pei-Hsuan and Tai, An-Shun and Kao, Chu-Lan Michael and Lin, Y… (2025) Causal mediation analysis for difference-in-difference design and panel data | 0.644 | 2 | 2 | 100% |
| 4 | Huber, Martin and Oberhänsli, Sarina Joy (2026) Difference-in-differences for mediation analysis using double machine learning | 0.644 | 2 | 2 | 100% |
| 5 | Schochet, Peter Z (2001) National Job Corps Study: the impacts of Job Corps on participants' employment and related outcomes | 0.644 | 2 | 2 | 100% |
| 6 | Schochet, Peter Z and Burghardt, John and McConnell, Sheena (2008) Does job corps work? Impact findings from the national Job Corps study | 0.644 | 2 | 2 | 100% |
| 7 | Zhang, Junni L and Rubin, Donald B and Mealli, Fabrizia (2009) Likelihood-based analysis of causal effects of job-training programs using principal stratification | 0.644 | 2 | 2 | 100% |
| 8 | Abadie, Alberto (2005) Semiparametric difference-in-differences estimators | 0.405 | 1 | 1 | 100% |
| 9 | Bickel, Peter J and Klaassen, Chris AJ and Bickel, Peter J and Ritov… (1993) Efficient and adaptive estimation for semiparametric models | 0.405 | 1 | 1 | 100% |
| 10 | Blanco, German and Flores, Carlos A and Flores-Lagunes, Alfonso (2013) Bounds on average and quantile treatment effects of Job Corps training on wages | 0.405 | 1 | 1 | 100% |
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