Regression modelQuasi-experimental / causal inference
Entropy Balancing
Entropy balancing is a preprocessing method for causal inference that assigns weights to control-group units so that the reweighted control sample matches the treatment group exactly on a chosen set of covariate moments (means, variances, skewness). Introduced by Hainmueller (2012), it replaces trial-and-error propensity-score trimming with a constrained maximum-entropy optimisation that achieves balance in a single step.
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Sources
- 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 ↗
- Zhao, Q., & Coey, D. (2017). Entropy balancing is doubly robust. Journal of Causal Inference, 5(1). (Working paper version widely cited; see also Zhao & Coey 2018, Stanford GSB Research Paper.) link ↗
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Bayesian Coarsened Exact MatchingBayesian Entropy BalancingBayesian Matching EstimatorBayesian Propensity Score MatchingCoarsened Exact MatchingDynamic Entropy BalancingHeterogeneous Treatment Effect Coarsened Exact MatchingHeterogeneous Treatment Effect Entropy BalancingHeterogeneous Treatment Effect Matching EstimatorMachine Learning-Augmented Coarsened Exact MatchingMachine Learning-Augmented Entropy BalancingMachine Learning-Augmented Propensity Score MatchingMulti-period Coarsened Exact MatchingPanel Data Entropy BalancingPanel Data Propensity Score MatchingPolicy Evaluation Coarsened Exact MatchingPropensity Score WeightingSpatial Entropy Balancing