Regression modelQuasi-experimental / causal inference

Panel Data Inverse Probability Weighting

Panel Data Inverse Probability Weighting (panel IPW) estimates the causal effect of a time-varying treatment by reweighting observed units to create a pseudo-population in which treatment is independent of measured confounders at each time point. It extends the cross-sectional IPW framework to longitudinal settings where treatment status and confounders both evolve across multiple periods.

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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. Boca Raton: Chapman & Hall/CRC. link

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Referenced by

ScholarGatePanel Data Inverse Probability Weighting (Panel Data Inverse Probability Weighting Estimator). Retrieved 2026-06-04 from https://scholargate.app/en/causal-inference/panel-data-inverse-probability-weighting