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Bayesian Doubly Robust Estimation/Evidence
Method evidence record

Bayesian Doubly Robust Estimation

Bayesian Doubly Robust Estimation combines the classical doubly robust (DR) augmented inverse probability weighting framework with Bayesian inference. It simultaneously models the propensity score and the outcome regression, placing prior distributions over both, and derives a posterior distribution over the average treatment effect that remains consistent even if one of the two component models is misspecified.

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Source record

Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.

Bayesian Doubly Robust Estimation of Average Treatment Effects
Taxonomic method record · regression-model / causal-inference
  • 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
  • Scharfstein, D., Nabi, R., Kennedy, E. H., Huang, M.-Y., Bonvini, M., & Smid, M. (2021). Semiparametric sensitivity analysis: Unmeasured confounding in observational studies. arXiv:1910.14694. · URL
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Related methods

Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.

Taxonomic bucketBayesian Causal Impact Analysismachine-suggested · Relational suggestion, not evidence.Taxonomic bucketBayesian Propensity Score Matchingmachine-suggested · Relational suggestion, not evidence.Same method familyDoubly Robust Estimationmachine-suggested · Relational suggestion, not evidence.Same method familyInverse Probability Weightingmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketMarginal Structural Modelmachine-suggested · Relational suggestion, not evidence.

Evidence status

Sources recorded, not reviewed

Bibliographic sources are present. Claim-level evidence review has not been performed.

Sources

2 recorded citations, copied from the method source record.

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