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Stacked Triple Differences

Meng Hsuan Hsieh

arXiv 24 Apr 2026 · Econometrics

arXiv:2604.22982 · PDF · DOI · OpenAlex · Extracted main text

Abstract

Triple differences (DDD) is a workhorse quasi-experimental design in applied economics. But, under staggered adoption, its conventional three-way fixed-effects (3WFE) implementation inherits the forbidden-comparison and interpretation issues now well understood in the difference-in-differences literature. To resolve these issues, I introduce stacked DDD. I extend the stacked difference-in-differences approach to the DDD setting by creating self-contained stacks, each consisting of four cells over an event window: treated and clean comparison cohorts, each with treatment-eligible and treatment-ineligible units. Appending these stacks yields a unified dataset for estimating treatment effects without making forbidden comparisons. I prove that, at each post-treatment event-time, a linear regression with fully saturated fixed-effects applied to the stacked dataset identifies a strictly positive, cell-size-weighted average of stack-level conditional average treatment effects, with stack weights proportional to stack-level cell sizes. Building on this characterization, I outline alternative weighting schemes that recover distinct, transparent causal estimands with clear interpretations. Stacked DDD complements recent GMM and imputation-based frameworks by trading efficiency for regression-based transparency, pairwise (rather than global) parallel trends, and direct control over aggregation weights. I provide two empirical illustrations where stacked DDD yields substantially different quantitative conclusions compared to existing procedures.

Citation extraction

27
references
95
in-text mentions
27
distinct cited
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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
1Shastry, Gauri Kartini and Tortorice, Daniel L (2025) Effective Health Aid: Evidence from Gavi's Vaccine Program1.000153100%
2Ortiz-Villavicencio, Marcelo and Sant'Anna, Pedro H. C (2025) Better Understanding Triple Differences Estimators0.9285480%
3de Chaisemartin, Clément and D'Haultfœuille, Xavier (2024) Difference-in-Differences Estimators of Intertemporal Treatment Effects0.92843100%
4Hansen, Casper Worm and Wingender, Asger Mose (2023) National and Global Impacts of Genetically Modified Crops0.9209678%
5de Chaisemartin, Clément and D’Haultfœuille, Xavier (2020) Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects0.8746467%
6Sun, Liyang and Abraham, Sarah (2021) Estimating dynamic treatment effects in event studies with heterogeneous treatment effects0.86517765%
7Strezhnev, Anton (2023) Decomposing Triple-Differences Regression under Staggered Adoption0.8435560%
8Callaway, Brantly and Sant’Anna, Pedro H.C (2021) Difference-in-Differences with multiple time periods0.84333100%
9Borusyak, Kirill and Jaravel, Xavier and Spiess, Jann (2024) Revisiting Event-Study Designs: Robust and Efficient Estimation0.81142100%
10Goodman-Bacon, Andrew (2021) Difference-in-differences with variation in treatment timing0.7547343%

Showing the top 10 of 27 scored citations.