Comparar métodos

	Equilíbrio de Entropia Aumentado por Aprendizado de Máquina	Propensity Score Matching
Área	Inferência causal	Estatística para pesquisa
Família	Regression model	Process / pipeline
Ano de origem	2012-2017	1983
Autor original	Hainmueller (2012) for entropy balancing; ML augmentation developed by Zhao & Percival (2017) and subsequent literature	Paul Rosenbaum and Donald Rubin
Tipo	Weighting-based causal estimator	Method
Fonte seminal	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.	Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.
Outros nomes	ML-EB, augmented entropy balancing, ML-augmented EB, doubly-robust entropy balancing	PSM, propensity score weighting, covariate balance
Relacionados	4	3
Resumo	Machine learning-augmented entropy balancing (ML-EB) combines Hainmueller's entropy balancing reweighting scheme with a machine-learning outcome model to produce a doubly-robust causal estimator. By jointly optimising covariate balance weights and a flexible predicted-outcome adjustment, ML-EB delivers consistent treatment-effect estimates even when either the weighting or the outcome model is misspecified, and it handles high-dimensional covariate spaces that classical entropy balancing cannot easily balance.	Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias.