Regression modelQuasi-experimental / causal inference

Multi-period Doubly Robust Estimation

Multi-period doubly robust (DR) estimation extends the classic doubly robust approach to longitudinal settings with multiple treatment periods and time points. It combines an outcome regression model and a propensity score model for each period, retaining consistency of the causal effect estimate as long as at least one of the two models is correctly specified at every time point.

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Sources

  1. Bang, H., & Robins, J. M. (2005). Doubly robust estimation in missing data and causal inference models. Biometrics, 61(4), 962-973. DOI: 10.1111/j.1541-0420.2005.00377.x
  2. Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230. DOI: 10.1016/j.jeconom.2020.12.001

Related methods

ScholarGateMulti-period Doubly Robust Estimation (Multi-period Doubly Robust Causal Effect Estimator). Retrieved 2026-06-04 from https://scholargate.app/en/causal-inference/multi-period-doubly-robust-estimation