Difference-in-Differences Designs: A Practitioner’s Guide

Difference-in-differences (DiD) is arguably the most popular quasi-experimental research design. Its canonical form, with two groups and two periods, is well understood. However, empirical practices can be ad hoc when researchers go beyond that simple case. This article provides an organizing framework for discussing different types of DiD designs and their associated DiD estimators. It discusses covariates, weights, handling multiple periods, and staggered treatments. The organizational framework, however, applies to other extensions of DiD methods as well. (JEL C23, H75, I12, I38)

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Publication Details

Journal
Journal of Economic Literature
Published
2026-05-29
DOI
https://doi.org/10.1257/jel.20251650
Primary Topic
Advanced Causal Inference Techniques
Type
article
Field-Weighted Citation Impact
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article

Difference-in-Differences Designs: A Practitioner’s Guide

Pedro H. C. Sant’Anna, Brantly Callaway, Andrew Baker, Andrew Goodman-Bacon et al.
Journal of Economic Literature
Advanced Causal Inference Techniques
article

Difference-in-Differences Designs: A Practitioner’s Guide

Pedro H. C. Sant’Anna, Brantly Callaway, Andrew Baker, Andrew Goodman-Bacon, Scott Cunningham
article en

Abstract

Difference-in-differences (DiD) is arguably the most popular quasi-experimental research design. Its canonical form, with two groups and two periods, is well understood. However, empirical practices can be ad hoc when researchers go beyond that simple case. This article provides an organizing framework for discussing different types of DiD designs and their associated DiD estimators. It discusses covariates, weights, handling multiple periods, and staggered treatments. The organizational framework, however, applies to other extensions of DiD methods as well. (JEL C23, H75, I12, I38)

Journal of Economic LiteratureVol. 64(2)
Federal Reserve Bank of Minneapolis (US), Emory University (US), Baylor University (US), University of California, Berkeley (US)
Openalex Percentile: Top 4%
Advanced Causal Inference Techniques
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