Порівняння методів

	Машинне навчання з аугментацією балансуванням ентропії	Зважування за оберненою ймовірністю лікування (IPW / IPTW)
Галузь	Причинно-наслідковий висновок	Причинно-наслідковий висновок
Родина	Regression model	Regression model
Рік появи	2012-2017	2000
Автор методу	Hainmueller (2012) for entropy balancing; ML augmentation developed by Zhao & Percival (2017) and subsequent literature	Robins, Hernán & Brumback
Тип	Weighting-based causal estimator	Causal inference weighting estimator
Основоположне джерело	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.	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
Інші назви	ML-EB, augmented entropy balancing, ML-augmented EB, doubly-robust entropy balancing	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Пов'язані	4	5
Підсумок	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.	Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias.