Methoden vergleichen

	Matching-Schätzer	Inverse Probability of Treatment Weighting (IPW / IPTW)
Fachgebiet	Kausale Inferenz	Kausale Inferenz
Familie	Regression model	Regression model
Entstehungsjahr	1973	2000
Urheber	Rubin (1973); large-sample theory by Abadie & Imbens (2006)	Robins, Hernán & Brumback
Typ	Nonparametric matching / causal inference	Causal inference weighting estimator
Wegweisende Quelle	Abadie, A., & Imbens, G. W. (2006). Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica, 74(1), 235-267.	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
Aliasnamen	nearest-neighbor matching, NNM, matching on covariates, covariate matching	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Verwandt	6	5
Zusammenfassung	The matching estimator identifies the causal effect of a treatment by pairing each treated unit with one or more untreated units that have similar observed characteristics. Formalised by Rubin (1973) and given rigorous large-sample theory by Abadie and Imbens (2006), it constructs a credible control group from observational data without requiring a parametric model for the outcome.	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.