References

The estimator

The method csdid implements, and the paper to cite for it:

Callaway, Brantly, and Pedro H. C. Sant’Anna. 2021. “Difference-in-Differences with Multiple Time Periods.” Journal of Econometrics 225 (2): 200–230. doi:10.1016/j.jeconom.2020.12.001

The two-period doubly robust estimator applied to each (g,t) cell, used by method(dr), the default:

Sant’Anna, Pedro H. C., and Jun Zhao. 2020. “Doubly Robust Difference-in-Differences Estimators.” Journal of Econometrics 219 (1): 101–122. doi:10.1016/j.jeconom.2020.06.003

The semiparametric foundation for conditioning on covariates:

Abadie, Alberto. 2005. “Semiparametric Difference-in-Differences Estimators.” Review of Economic Studies 72 (1): 1–19. doi:10.1111/0034-6527.00321

The efficiency theory for these designs, which shows that the tightest estimator depends on the covariance structure of the outcomes:

Chen, Xiaohong, Pedro H. C. Sant’Anna, and Haitian Xie. 2025. “Efficient Difference-in-Differences and Event Study Estimators.” arXiv:2506.17729

None of the estimators csdid offers attains that bound outside special cases. Thus method(dr) is a sensible default, and it is not an efficient estimator in general.

Reviews

These two reviews cover the DiD literature more broadly than any single estimator does, and either is a reasonable place to start. The first gives the organizing framework for DiD designs and the estimators built on them, and every example on this site uses its replication data. The second surveys the recent econometrics literature and the estimators it has produced.

Baker, Andrew, Brantly Callaway, Scott Cunningham, Andrew Goodman-Bacon, and Pedro H. C. Sant’Anna. 2026. “Difference-in-Differences Designs: A Practitioner’s Guide.” Journal of Economic Literature 64 (2): 498–557. doi:10.1257/jel.20251650

Roth, Jonathan, Pedro H. C. Sant’Anna, Alyssa Bilinski, and John Poe. 2023. “What’s Trending in Difference-in-Differences? A Synthesis of the Recent Econometrics Literature.” Journal of Econometrics 235 (2): 2218–2244. doi:10.1016/j.jeconom.2023.03.008

Two-way fixed effects under staggered timing

These papers show why a TWFE coefficient is not the average treatment effect on the treated when timing is staggered and effects are heterogeneous, and they differ in how they decompose the resulting bias into comparisons that are and are not valid. We work through the argument in Why not two-way fixed effects.

Goodman-Bacon, Andrew. 2021. “Difference-in-Differences with Variation in Treatment Timing.” Journal of Econometrics 225 (2): 254–277. doi:10.1016/j.jeconom.2021.03.014

de Chaisemartin, Clément, and Xavier D’Haultfœuille. 2020. “Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects.” American Economic Review 110 (9): 2964–2996. doi:10.1257/aer.20181169

Sun, Liyang, and Sarah Abraham. 2021. “Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects.” Journal of Econometrics 225 (2): 175–199. doi:10.1016/j.jeconom.2020.09.006

Citing csdid

@misc{csdidStata,
  title  = {csdid: Difference-in-Differences with Multiple Time Periods in Stata},
  note   = {Stata module, version 2.0.0},
  year   = {2026},
  url    = {https://github.com/pedrohcgs/csdid-stata}
}