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| Penimbangan Skor Propensitas Evaluasi Kebijakan× | Estimasi Robust Ganda (AIPW)× | |
|---|---|---|
| Bidang | Inferensi Kausal | Inferensi Kausal |
| Keluarga | Regression model | Regression model |
| Tahun asal≠ | 1983/2003 | 2005 |
| Pencetus≠ | Rosenbaum & Rubin (1983); extended to policy evaluation by Hirano, Imbens & Ridder (2003) | Robins & Rotnitzky; Bang & Robins |
| Tipe≠ | Quasi-experimental causal inference | Semiparametric causal estimator |
| Sumber perintis≠ | 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 | PSW policy evaluation, inverse probability weighting for policy, IPW policy evaluation, policy PSW | AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW) |
| Terkait≠ | 6 | 5 |
| Ringkasan≠ | Policy evaluation propensity score weighting applies inverse-probability weighting to observational data to estimate the causal effect of a policy program. By reweighting participants and non-participants so they resemble a target population, it removes selection bias from voluntary or administratively allocated program assignment without requiring randomization. | 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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