Regression modelQuasi-experimental / causal inference

Bayesian Entropy Balancing

Bayesian Entropy Balancing extends the classical entropy balancing approach — which reweights control units so that their covariate moments match the treated group exactly — by embedding this reweighting within a Bayesian framework. This allows researchers to incorporate prior beliefs about treatment propensities, propagate parameter uncertainty into the final causal estimate, and obtain credible intervals rather than only classical confidence intervals.

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Sources

  1. Hainmueller, J. (2012). Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies. Political Analysis, 20(1), 25-46. DOI: 10.1093/pan/mpr025
  2. Vegetabile, B. G., Griffin, B. A., Coffman, D. L., Cefalu, M., Robbins, M. W., & McCaffrey, D. F. (2021). Nonparametric estimation of population average dose-response curves using entropy balancing weights for continuous exposures. Health Services and Outcomes Research Methodology, 21(1), 69-110. DOI: 10.1007/s10742-020-00236-2

Related methods

ScholarGateBayesian Entropy Balancing (Bayesian Entropy Balancing for Causal Inference). Retrieved 2026-06-04 from https://scholargate.app/en/causal-inference/bayesian-entropy-balancing