This post provides an up-to-date overview of Difference-In-Difference tools, papers, and other links. What is important here is not the timeliness but the relevance. While there are new methods daily, only a few are used in practice (or demanded by reviewers). The idea is that not only the advantages of each method are given here, but also the disadvantages. Unfortunately, disadvantages of individual methods (e.g. scalability to larger data sets) are rarely communicated.


Since most of the tools were developed by statisticians, there is also quite strong support for R. There seem to be Python implementations of some methods, especially, CausalImpact and a diverse set of Double ML approaches. In other languages like Julia, support still needs improvement. While the language has clear advantages and also offers high-speed fixed effects libraries (even with GPU support!), I am not aware of any libraries for propensity score matching or inverse probability weighting. Especially considering the rapid development in methods, I would therefore advise the use of R.

Overviews & Tutorials

While online tutorials are numerous, I will link those that helped me here:



  • Is it easy to add controls to a DiD? Not always
  • Is it appropriate to use a log-transformed DV in a DiD setting? It is often done, but there are some issues


Here are listed those papers that you should know.

  • Roth, J., Sant’Anna, P. H., Bilinski, A., & Poe, J. (2023). What’s trending in difference-in-differences? A synthesis of the recent econometrics literature. Journal of Econometrics. PDF
  • Goldfarb, A., Tucker, C., & Wang, Y. (2022). Conducting research in marketing with quasi-experiments. Journal of Marketing, 86(3), 1-20. PDF
  • Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015). Inferring causal impact using Bayesian structural time-series models. The Annals of Applied Statistics, 247-274. PDF
  • Wager, S., & Athey, S. (2018). Estimation and inference of heterogeneous treatment effects using random forests. Journal of the American Statistical Association, 113(523), 1228-1242. PDF
  • Athey, S., & Imbens, G. W. (2017). The state of applied econometrics: Causality and policy evaluation. Journal of Economic perspectives, 31(2), 3-32. PDF
  • Callaway, B., & Sant’Anna, P. H. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230. PDF
  • Sun, L., & Abraham, S. (2021). Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. Journal of Econometrics, 225(2), 175-199. PDF
  • Roth, J., & Sant’Anna, P. H. (2023). When is parallel trends sensitive to functional form?. Econometrica, 91(2), 737-747. PDF
  • De Chaisemartin, C., & d’Haultfoeuille, X. (2023). Two-way fixed effects and differences-in-differences with heterogeneous treatment effects: A survey. The Econometrics Journal, 26(3), C1-C30. PDF
  • Rambachan, A., & Roth, J. (2023). A more credible approach to parallel trends. Review of Economic Studies, rdad018. PDF


  • Pearl, J. (2009). Causality. Cambridge university press.
  • Cunningham, S. (2021). Causal inference: The mixtape. Yale university press.
  • Angrist, J. D., & Pischke, J. S. (2009). Mostly harmless econometrics: An empiricist’s companion. Princeton university press.