Hi everyone,
A couple of people have approached me with requests for additional readings on causal inference with panel, time-series data. Keep in mind that Jake's slides from Days 13 - 14 address this topic, with the idea that properly stratified, matched data can be analyzed as a random experiment. However, IF you still want to incorporate temporal dependence in your project, check out these additional topics:
(1) Counterfactual estimators (see also, Jake's slides)
Liu, L., Wang, Y., & Xu, Y. (2021). A practical guide to counterfactual estimators for causal inference with time-series cross-sectional data. arXiv preprint arXiv:2107.00856.
(2) Risk set matching
Li, Y. P., Propert, K. J., & Rosenbaum, P. R. (2001). Balanced risk set matching. Journal of the American Statistical Association, 96(455), 870-882.
Thomas, L. E., Yang, S., Wojdyla, D., & Schaubel, D. E. (2020). Matching with time‐dependent treatments: A review and look forward. Statistics in medicine, 39(17), 2350-2370.
(3) Prognostic propensity scores
Hansen, B. B. (2008). The prognostic analogue of the propensity score. Biometrika, 95(2), 481-488.
(4) Difference-in-difference designs with multiple treatment times
Mackenzie, D. (2020). Revisiting the Difference-in-Differences Parallel Trends Assumption: Part II What happens if the parallel trends assumption is (might be) violated? https://blogs.worldbank.org/impactevaluations/revisiting-difference-differences-parallel-trends-assumption-part-ii-what-happens.
Roth, J., Sant'Anna, P. H., Bilinski, A., & Poe, J. (2022). What's Trending in Difference-in-Differences? A Synthesis of the Recent Econometrics Literature. arXiv preprint arXiv:2201.01194.
(5) General review of matching with observational data
Rosenbaum, P. R. (2020). Modern algorithms for matching in observational studies. Annual Review of Statistics and Its Application, 7, 143-176.
Finally, for those who want a layman's perspective on causal inference using quasi-experimental designs, check out:
(6) Angrist, J. D., & Pischke, J. S. (2009). Mostly harmless econometrics: An empiricist's companion. Princeton university press.