Сравнение методов

	Динамическая энтропийная балансировка	Энтропийное балансирование
Область	Причинно-следственный вывод	Причинно-следственный вывод
Семейство	Regression model	Regression model
Год появления	2012-2018	2012
Автор метода	Hainmueller (2012) for static entropy balancing; extended to dynamic settings by Blackwell and Glynn (2018) and subsequent methodologists	Jens Hainmueller
Тип	Causal inference / weighting estimator	Covariate-balancing reweighting
Основополагающий источник	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.	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.
Другие названия	DEB, longitudinal entropy balancing, entropy balancing with time-varying treatment, sequential entropy balancing	EB, entropy reweighting, covariate balancing via entropy, Hainmueller balancing
Связанные	6	6
Сводка	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.	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.