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A Way to Synthetic Triple Difference

Castiel Chen Zhuang

arXiv 18 Sep 2024 · Econometrics

arXiv:2409.12353 · PDF · DOI · OpenAlex · Extracted main text

Abstract

This paper discusses a practical approach that combines synthetic control with triple difference to address violations of the parallel trends assumption. By transforming triple difference into a DID structure, we can apply synthetic control to a triple-difference framework, enabling more robust estimates when parallel trends are violated across multiple dimensions. The proposed procedure is applied to a real-world dataset to illustrate when and how we should apply this practice, while cautions are presented afterwards. This method contributes to improving causal inference in policy evaluations and offers a valuable tool for researchers dealing with heterogeneous treatment effects across subgroups.

Citation extraction

6
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13
in-text mentions
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main-text words

appendix boundary found by appendix_command · 73% of the source is main text. Read the extracted text to check this.

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
1Dmitry Arkhangelsky, Susan Athey, David A. Hirshberg, Guido W. Imben… (2021) Synthetic difference-in-differences1.00063100%
2Andreas Olden and Jarle Møen (2022) The triple difference estimator0.73732100%
3Alberto Abadie, Alexis Diamond, and Jens Hainmueller (2009) Synthetic control methods for comparative case studies: Estimating the effect of California’s Tobacco Control Program0.40511100%
4Joshua D. Angrist and Jörn-Steffen Pischke (2009) Mostly Harmless Econometrics: An Empiricist's Companion0.40511100%
5Marianne Bertrand, Esther Duflo, and Sendhil Mullainathan (2004) How Much Should We Trust Differences-In-Differences Estimates?0.40511100%
6Jens Hainmueller (2012) Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies0.40511100%

Showing the top 6 of 6 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
1Semiparametric Triple Difference Estimators0.51121