Regression modelQuasi-experimental / causal inference

Policy Evaluation Inverse Probability Weighting

Policy evaluation inverse probability weighting (IPW) uses estimated propensity scores to reweight observed units so that the weighted sample mimics a randomised experiment. Each unit is weighted by the inverse of its probability of receiving the policy, creating a pseudo-population in which treatment assignment is independent of observed covariates and the average treatment effect (ATE) can be read off directly.

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Sources

  1. Imbens, G. W., & Wooldridge, J. M. (2009). Recent Developments in the Econometrics of Program Evaluation. Journal of Economic Literature, 47(1), 5-86. DOI: 10.1257/jel.47.1.5
  2. 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

Related methods

ScholarGatePolicy Evaluation Inverse Probability Weighting (Inverse Probability Weighting for Policy Evaluation). Retrieved 2026-06-04 from https://scholargate.app/en/causal-inference/policy-evaluation-inverse-probability-weighting