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Regression modelQuasi-experimental / causal inference

Policy Evaluation Doubly Robust Estimation

Policy Evaluation Doubly Robust Estimation applies the doubly robust (DR) estimator to assess the causal effect of a public policy or programme. It combines a model of treatment assignment (propensity score) with a model of the outcome, and requires only one of the two models to be correctly specified to produce a consistent estimate of the average treatment effect, making it a resilient tool for programme evaluation.

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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. Robins, J. M., Rotnitzky, A., & Zhao, L. P. (1994). Estimation of regression coefficients when some regressors are not always observed. Journal of the American Statistical Association, 89(427), 846-866. DOI: 10.1080/01621459.1994.10476818 ↗

Related methods

Doubly Robust EstimationInverse Probability WeightingMarginal Structural ModelPolicy Evaluation Propensity Score MatchingPropensity Score Weighting

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ScholarGate — Policy Evaluation Doubly Robust Estimation (Doubly Robust Estimation for Policy Evaluation). Retrieved 2026-06-04 from https://scholargate.app/en/causal-inference/policy-evaluation-doubly-robust-estimation