Generalized difference-in-differences Article Swipe
YOU?
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· 2023
· Open Access
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· DOI: https://doi.org/10.48550/arxiv.2312.05400
We propose a new method for estimating causal effects in longitudinal/panel data settings that we call generalized difference-in-differences. Our approach unifies two alternative approaches in these settings: ignorability estimators (e.g., synthetic controls) and difference-in-differences (DiD) estimators. We propose a new identifying assumption -- a stable bias assumption -- which generalizes the conditional parallel trends assumption in DiD, leading to the proposed generalized DiD framework. This change gives generalized DiD estimators the flexibility of ignorability estimators while maintaining the robustness to unobserved confounding of DiD. We also show how ignorability and DiD estimators are special cases of generalized DiD. We then propose influence-function based estimators of the observed data functional, allowing the use of double/debiased machine learning for estimation. We also show how generalized DiD easily extends to include clustered treatment assignment and staggered adoption settings, and we discuss how the framework can facilitate estimation of other treatment effects beyond the average treatment effect on the treated. Finally, we provide simulations which show that generalized DiD outperforms ignorability and DiD estimators when their identifying assumptions are not met, while being competitive with these special cases when their identifying assumptions are met.
Related Topics
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2312.05400
- https://arxiv.org/pdf/2312.05400
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389649865
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4389649865Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2312.05400Digital Object Identifier
- Title
-
Generalized difference-in-differencesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-12-08Full publication date if available
- Authors
-
Denis Agniel, Max Rubinstein, Jessie Coe, Maria DeYoreoList of authors in order
- Landing page
-
https://arxiv.org/abs/2312.05400Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2312.05400Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2312.05400Direct OA link when available
- Concepts
-
Estimator, Computer science, Average treatment effect, Robustness (evolution), Flexibility (engineering), Mathematics, Econometrics, Mathematical optimization, Statistics, Gene, Chemistry, BiochemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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