Confronta i metodi

	Stimatore di Matching Robusto (Matching con Correzione del Bias)	Abbinamento del punteggio di propensione
Campo	Inferenza causale	Statistica per la ricerca
Famiglia	Regression model	Process / pipeline
Anno di origine	2006/2011	1983
Ideatore	Abadie & Imbens	Paul Rosenbaum and Donald Rubin
Tipo	Causal inference / matching	Method
Fonte seminale	Abadie, A., & Imbens, G. W. (2011). Bias-Corrected Matching Estimators for Average Treatment Effects. Journal of Business & Economic Statistics, 29(1), 1-11.	Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.
Alias	bias-corrected matching, Abadie-Imbens matching, AI matching estimator, robust nearest-neighbor matching	PSM, propensity score weighting, covariate balance
Correlati	6	3
Sintesi	The robust matching estimator, developed by Abadie and Imbens (2006, 2011), extends nearest-neighbor matching by adding a regression-based bias correction that removes the finite-sample bias arising when matched units are not perfectly alike. It yields consistent, asymptotically normal estimates of average treatment effects with a heteroskedasticity-robust variance formula that is valid regardless of the number of continuous covariates.	Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias.