Sammenlign metoder

	Entropy Balancing	Inverse Probability of Treatment Weighting (IPW / IPTW)
Fagfelt	Kausal inferens	Kausal inferens
Familie	Regression model	Regression model
Opprinnelsesår	2012	2000
Opphavsperson	Jens Hainmueller	Robins, Hernán & Brumback
Type	Covariate-balancing reweighting	Causal inference weighting estimator
Opprinnelig 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.
Alias	EB, entropy reweighting, covariate balancing via entropy, Hainmueller balancing	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Relaterte	6	5
Sammendrag	Entropy balancing is a preprocessing method for causal inference that assigns weights to control-group units so that the reweighted control sample matches the treatment group exactly on a chosen set of covariate moments (means, variances, skewness). Introduced by Hainmueller (2012), it replaces trial-and-error propensity-score trimming with a constrained maximum-entropy optimisation that achieves balance in a single step.	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.