Regression modelQuasi-experimental / causal inference

Multi-period Inverse Probability Weighting

Multi-period Inverse Probability Weighting (IPW) estimates the causal effect of a treatment that varies across multiple time periods by reweighting observations according to the probability of receiving each period's treatment given past treatment history and time-varying confounders. It creates a pseudo-population where treatment at each period is independent of measured confounders, enabling unbiased estimation of sustained treatment strategies.

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Sources

  1. Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560. DOI: 10.1097/00001648-200009000-00011
  2. Hernan, M. A., & Robins, J. M. (2020). Causal Inference: What If. Chapman and Hall/CRC. link

Related methods

ScholarGateMulti-period Inverse Probability Weighting (Multi-period Inverse Probability Weighting Estimator). Retrieved 2026-06-04 from https://scholargate.app/en/causal-inference/multi-period-inverse-probability-weighting