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Using Pre-Trends for Inference in Difference-in-Differences

Clément de Chaisemartin

arXiv 23 Jul 2026 · Econometrics

arXiv:2607.21312 · PDF · Extracted main text

Abstract

Difference-in-differences (DID) are sometimes estimated with many pre-treatment periods. In such settings, the observed pre-treatment outcome evolutions provide direct information about the magnitude of shocks that could also occur after treatment. This paper proposes a simple inference procedure that uses those pre-trends as the reference distribution for the post-treatment DID. The procedure is closely related to existing conformal inference procedures, but its DID-specific predictor leads to a distinct identifying restriction. Existing procedures assume parallel trends, while this paper's procedure does not require parallel trends.

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13
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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.

ReferenceIntensityMentionsSectionsMain text
1Chernozhukov, V., Wüthrich, K., and Zhu, Y (2021) An exact and robust conformal inference method for counterfactual and synthetic controls0.69361100%
2Chernozhukov, V., Wüthrich, K., and Zhu, Y (2018) Exact and robust conformal inference methods for predictive machine learning with dependent data0.64422100%
3Vovk, V., Gammerman, A., and Shafer, G (2005) Algorithmic Learning in a Random World0.64422100%
4Rambachan, A., and Roth, J (2023) A more credible approach to parallel trends0.51121100%
5Andrews, D.\ W.\ K (2003) End-of-sample instability tests0.40511100%
6Bilinski, A., and Hatfield, L.\ A (2018) Nothing to see here? Non-inferiority approaches to parallel trends and other model assumptions0.40511100%
7Conley, T.\ G., and Taber, C.\ R (2011) difference in differences0.40511100%
8Dette, H., and Schumann, M (2024) Testing for equivalence of pre-trends in difference-in-differences estimation0.40511100%
9Ferman, B., and Pinto, C (2019) Inference in differences-in-differences with few treated groups and heteroskedasticity0.40511100%
10Lei, L., and Candès, E.\ J (2021) Conformal inference of counterfactuals and individual treatment effects0.40511100%

Showing the top 10 of 13 scored citations.

Cited by, within the corpus

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

Citing paperIntensityMentionsSections
1Trading Scope for Credibility in Difference-in-Differences0.40511