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Regression modelQuasi-experimental / causal inference

Dynamic Entropy Balancing

Dynamic Entropy Balancing extends the entropy balancing reweighting approach to settings with time-varying treatments in panel or longitudinal data. It constructs unit weights at each time period such that the covariate distributions of treated and comparison units are balanced on specified moments, adjusting sequentially for prior treatment history and time-varying confounders to estimate the causal effect of treatment sequences on outcomes.

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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. Blackwell, M., & Glynn, A. N. (2018). How to Make Causal Inferences with Time-Series Cross-Sectional Data under Selection on Observables. American Political Science Review, 112(4), 1067-1082. DOI: 10.1017/S0003055418000357 ↗

Related methods

Dynamic Inverse Probability WeightingDynamic Propensity Score MatchingEntropy BalancingInverse Probability WeightingMarginal Structural ModelPropensity Score Weighting

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ScholarGate — Dynamic Entropy Balancing (Dynamic Entropy Balancing for Longitudinal Causal Inference). Retrieved 2026-06-04 from https://scholargate.app/en/causal-inference/dynamic-entropy-balancing