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| Kettősen robusztus becslés (AIPW)× | Regresszió Ordináris Legkisebb Négyzetes (OLS) módszerrel× | |
|---|---|---|
| Tudományterület≠ | Oksági következtetés | Ökonometria |
| Módszercsalád | Regression model | Regression model |
| Keletkezés éve≠ | 2005 | 2019 |
| Megalkotó≠ | Robins & Rotnitzky; Bang & Robins | Wooldridge (textbook treatment); classical least squares |
| Típus≠ | Semiparametric causal estimator | Linear regression |
| Alapmű≠ | 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 ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Alternatív nevek | AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Kapcsolódó | 5 | 5 |
| Összefoglaló≠ | 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. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
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