Uporedite metode

	Heterogena obrada efekata balansiranjem entropije	Metoda inverzne verovatnoće tretmana (IPW / IPTW)
Oblast	Kauzalno zaključivanje	Kauzalno zaključivanje
Porodica	Regression model	Regression model
Godina nastanka	2012-2016	2000
Tvorac	Hainmueller (2012) for entropy balancing; Athey & Imbens (2016) for heterogeneous effect estimation	Robins, Hernán & Brumback
Tip	Causal inference / heterogeneous effect estimation	Causal inference weighting estimator
Temeljni izvor	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.
Drugi nazivi	HTE entropy balancing, CATE with entropy balancing, heterogeneous effects EB, subgroup entropy balancing	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Srodne	5	5
Sažetak	Heterogeneous Treatment Effect Entropy Balancing combines entropy balancing — a preprocessing step that reweights control units to match the treatment group on covariate moments — with methods that estimate how the treatment effect varies across subgroups or individuals. It produces covariate-balanced weights without parametric propensity models, then uses those weights to estimate conditional average treatment effects (CATEs) across moderating variables.	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.