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	Valutazione di politiche mediante ponderazione per l'inverso della probabilità	Inverse Probability of Treatment Weighting (IPW / IPTW)
Campo	Inferenza causale	Inferenza causale
Famiglia	Regression model	Regression model
Anno di origine	1952 (IPW origin); 2000s (policy evaluation application)	2000
Ideatore	Horvitz & Thompson (1952); extended to causal policy settings by Robins, Hernan & Brumback (2000) and Imbens & Wooldridge (2009)	Robins, Hernán & Brumback
Tipo	Reweighting estimator for causal policy analysis	Causal inference weighting estimator
Fonte seminale	Imbens, G. W., & Wooldridge, J. M. (2009). Recent Developments in the Econometrics of Program Evaluation. Journal of Economic Literature, 47(1), 5-86.	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
Alias	IPW policy evaluation, propensity-weighted policy analysis, inverse probability of treatment weighting	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Correlati	6	5
Sintesi	Policy evaluation inverse probability weighting (IPW) uses estimated propensity scores to reweight observed units so that the weighted sample mimics a randomised experiment. Each unit is weighted by the inverse of its probability of receiving the policy, creating a pseudo-population in which treatment assignment is independent of observed covariates and the average treatment effect (ATE) can be read off directly.	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.