Sammenlign metoder

	Heterogen Behandlingseffekt Entropivægtning	Vægtning med den inverse behandlingssandsynlighed (IPW / IPTW)
Fagområde	Kausal inferens	Kausal inferens
Familie	Regression model	Regression model
Oprindelsesår	2012-2016	2000
Ophavsperson	Hainmueller (2012) for entropy balancing; Athey & Imbens (2016) for heterogeneous effect estimation	Robins, Hernán & Brumback
Type	Causal inference / heterogeneous effect estimation	Causal inference weighting estimator
Oprindelig kilde	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.
Aliasser	HTE entropy balancing, CATE with entropy balancing, heterogeneous effects EB, subgroup entropy balancing	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Relaterede	5	5
Resumé	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.