What’s Trending in Difference-in-Differences? A Synthesis of the Recent Literature
Causal Question / Estimand
A synthesis, not a single estimand. Organizes the modern DiD literature around the group-time / dynamic treatment effects (Causal-Estimand) and the assumptions under which they are identified, providing a unified framework and practical roadmap.
Identification Strategy
Lays out the assumptions DiD rests on — Parallel-Trends (unconditional vs Conditional-Parallel-Trends) and No-Anticipation — and then surveys: (i) the TWFE-under-staggered-timing problem and Negative-Weighting; (ii) heterogeneity- robust estimators; (iii) tools for assessing/relaxing parallel trends (pre-tests done right, sensitivity analysis); and (iv) inference/clustering. A decision-oriented map of the field.
Key Assumptions
Parallel-Trends / Conditional-Parallel-Trends, No-Anticipation, and an explicit accounting of Treatment-Effect-Heterogeneity; valid comparison groups and clustering for inference. SUTVA throughout.
Threats to Validity
n/a — survey. Its through-line of cautions: don’t trust unsaturated TWFE under staggered timing; don’t treat pre-trend tests as sufficient; cluster appropriately; match the estimator to the design.
Setting / Data
n/a — methodological synthesis with worked guidance and references to software
(did, HonestDiD, did_multiplegt, etc.).
Key Claims
- A single framework ties together the staggered-timing, parallel-trends, and inference strands of the recent DiD literature.
- Practical recommendations: pick a heterogeneity-robust estimator, use honest sensitivity analysis for parallel trends, and cluster inference correctly.
- Serves as the field’s orientation map circa 2023.
Connections
- Synthesizes: GoodmanBacon2021-DiDVariationInTiming, SunAbraham2021-EventStudies, CallawaySantAnna2021-DiDMultiplePeriods, SantAnnaZhao2020-DoublyRobustDiD, RothPretrends2022-PretestWithCaution, RambachanRoth2023-MoreCredibleParallelTrends, DeChaisemartinDHaultfoeuille2023-TWFESurvey
- Inference lineage from: Bertrand2004-HowMuchShouldWeTrustDiD
- Companion practitioner guidance: BakerEtAl2025-DiDPractitionerGuide
- Intuitive walkthrough: RothSantAnna2023-WhatsTrendingInDiD-Explainer
- Slides: RothSantAnna2023-WhatsTrendingInDiD-Slides
- See also: DiD
Citation
Roth, J., Sant’Anna, P. H. C., Bilinski, A., & Poe, J. (2023). What’s Trending in Difference-in-Differences? A Synthesis of the Recent Econometrics Literature. Journal of Econometrics, 235(2), 2218–2244. https://doi.org/10.1016/j.jeconom.2023.03.008