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| Stimatore per Matching× | Inverse Probability of Treatment Weighting (IPW / IPTW)× | |
|---|---|---|
| Campo | Inferenza causale | Inferenza causale |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 1973 | 2000 |
| Ideatore≠ | Rubin (1973); large-sample theory by Abadie & Imbens (2006) | Robins, Hernán & Brumback |
| Tipo≠ | Nonparametric matching / causal inference | Causal inference weighting estimator |
| Fonte seminale≠ | Abadie, A., & Imbens, G. W. (2006). Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica, 74(1), 235-267. DOI ↗ | Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560. DOI ↗ |
| Alias≠ | nearest-neighbor matching, NNM, matching on covariates, covariate matching | IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting |
| Correlati≠ | 6 | 5 |
| Sintesi≠ | 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. |
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