Compara mètodes

	Equilibratge d'Entropia Augmentat per Aprenentatge Automàtic	Pes pesat per la probabilitat inversa (IPW / IPTW)
Camp	Inferència causal	Inferència causal
Família	Regression model	Regression model
Any d'origen	2012-2017	2000
Autor original	Hainmueller (2012) for entropy balancing; ML augmentation developed by Zhao & Percival (2017) and subsequent literature	Robins, Hernán & Brumback
Tipus	Weighting-based causal estimator	Causal inference weighting estimator
Font 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.	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
Àlies	ML-EB, augmented entropy balancing, ML-augmented EB, doubly-robust entropy balancing	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Relacionats	4	5
Resum	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.