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	Inverse Probability Weighting per Effetti Trattamento Eterogenei (HTE-IPW)	Ponderazione basata sul punteggio di propensione (PSW / IPW)
Campo	Inferenza causale	Inferenza causale
Famiglia	Regression model	Regression model
Anno di origine	2003–2015	1983 (propensity score); 2003 (efficient IPW estimator)
Ideatore	Hirano, Imbens & Ridder; further developed by Abrevaya, Hsu & Lieli	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)
Tipo	Causal inference / weighted regression	Causal inference / reweighting
Fonte seminale	Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient estimation of average treatment effects using the estimated propensity score. Econometrica, 71(4), 1161-1189.	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	HTE-IPW, CATE-IPW, heterogeneous IPW, conditional effect IPW	PSW, inverse probability weighting, IPW, propensity-based weighting
Correlati	5	6
Sintesi	HTE-IPW extends standard inverse probability weighting to recover how causal effects vary across subgroups or covariate values. By reweighting each observation by the inverse of its estimated treatment probability, the method creates a pseudo-population in which treatment is independent of background characteristics, and then estimates conditional average treatment effects (CATEs) as a function of those characteristics.	Propensity score weighting is a causal-inference method that reweights observations so that the covariate distributions of treated and untreated units look exchangeable, enabling unbiased estimation of average treatment effects from observational data. Each unit receives a weight that is the inverse of its probability of receiving the treatment it actually received — a strategy formalised by Rosenbaum and Rubin (1983) and given its efficient semiparametric form by Hirano, Imbens and Ridder (2003).