Sammenlign metoder
Gjennomgå de valgte metodene side om side; rader som avviker, er uthevet.
| Romlig invers sannsynlighet-vekting (Romlig IPW)× | Dobbel robust estimering (AIPW)× | |
|---|---|---|
| Fagfelt | Kausal inferens | Kausal inferens |
| Familie | Regression model | Regression model |
| Opprinnelsesår≠ | 2010s | 2005 |
| Opphavsperson≠ | Extension of Rosenbaum & Rubin (1983) IPW to spatial settings; formal treatment by Papadogeorgou et al. (2019) | Robins & Rotnitzky; Bang & Robins |
| Type≠ | Quasi-experimental / causal inference | Semiparametric causal estimator |
| Opprinnelig kilde≠ | Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score. Econometrica, 71(4), 1161-1189. DOI ↗ | 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 ↗ |
| Alias | Spatial IPW, Geographic IPW, Spatially-weighted IPW, SIPW | AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW) |
| Relaterte≠ | 6 | 5 |
| Sammendrag≠ | Spatial Inverse Probability Weighting extends the classical IPW estimator to settings where units are geo-referenced and spatial location is a confounding dimension. By incorporating geographic coordinates or spatial proximity into the propensity score model, it reweights the observed sample so that treatment and control groups are balanced not only on measured covariates but also on spatial structure, enabling credible causal inference from spatially indexed observational data. | 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. |
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