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| Estimació Doblement Robusta (AIPW)× | Emparellament per puntuació de propensió× | |
|---|---|---|
| Camp≠ | Inferència causal | Estadística per a la recerca |
| Família≠ | Regression model | Process / pipeline |
| Any d'origen≠ | 2005 | 1983 |
| Autor original≠ | Robins & Rotnitzky; Bang & Robins | Paul Rosenbaum and Donald Rubin |
| Tipus≠ | Semiparametric causal estimator | Method |
| Font seminal≠ | Robins, J. M. & Rotnitzky, A. (1995). Semiparametric Efficiency in Multivariate Regression Models with Missing Data. Journal of the American Statistical Association, 90(429), 122-129. DOI ↗ | 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. DOI ↗ |
| Àlies≠ | AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW) | PSM, propensity score weighting, covariate balance |
| Relacionats≠ | 5 | 3 |
| Resum≠ | Doubly Robust Estimation, also called Augmented Inverse Probability Weighting (AIPW), is a semiparametric method for estimating causal treatment effects that combines an outcome regression model with a propensity (treatment) model. Developed in the work of Robins & Rotnitzky (1995) and Bang & Robins (2005), it stays consistent as long as at least one of the two models is correctly specified. | 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. |
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