Compara mètodes

	Emparellament Robst de Puntuació de Propensió	Pes pesat per la probabilitat inversa (IPW / IPTW)
Camp	Inferència causal	Inferència causal
Família	Regression model	Regression model
Any d'origen	2016 (robust variance correction); 1983 (PSM foundations)	2000
Autor original	Abadie & Imbens (2016) for matching-on-estimated-propensity-score with corrected variance; Rosenbaum & Rubin (1983) for PSM foundations	Robins, Hernán & Brumback
Tipus	Quasi-experimental matching estimator with robust inference	Causal inference weighting estimator
Font seminal	Abadie, A., & Imbens, G. W. (2016). Matching on the Estimated Propensity Score. Econometrica, 84(2), 781-807.	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
Àlies	robust PSM, PSM with robust variance, bias-corrected PSM, matching with robust inference	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Relacionats	6	5
Resum	Robust Propensity Score Matching (robust PSM) is a quasi-experimental causal inference method that pairs treated and control units on their estimated probability of receiving treatment (the propensity score), then estimates the average treatment effect using variance estimators that account for the uncertainty introduced by estimating the propensity score itself. The correction, developed by Abadie and Imbens (2016), prevents misleading inference that standard bootstrap or analytic formulas produce when applied naively after matching.	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.