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| Trọng số điểm xu hướng mạnh mẽ× | Phân tích độ nhạy cho tính nhân quả× | |
|---|---|---|
| Lĩnh vực | Suy luận nhân quả | Suy luận nhân quả |
| Họ | Regression model | Regression model |
| Năm ra đời≠ | 1994–2019 | 1983–2002 |
| Người khởi xướng≠ | Robins, Rotnitzky, & Zhao (foundational augmented IPW); Zhao, Small, & Bhattacharya (sensitivity-robust IPW) | Paul R. Rosenbaum (hidden-bias framework); extended by Cinelli & Hazlett (omitted-variable approach) |
| Loại≠ | Robust causal weighting estimator | Diagnostic / robustness check |
| Công trình gốc≠ | Robins, J. M., Rotnitzky, A., & Zhao, L. P. (1994). Estimation of regression coefficients when some regressors are not always observed. Journal of the American Statistical Association, 89(427), 846-866. DOI ↗ | Rosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer. ISBN: 978-0387989679 |
| Tên gọi khác | robust PSW, robust IPW, robustness-augmented propensity score weighting, misspecification-robust weighting | sensitivity analysis, hidden-bias sensitivity analysis, Rosenbaum sensitivity analysis, omitted-variable sensitivity |
| Liên quan≠ | 6 | 4 |
| Tóm tắt≠ | Robust Propensity Score Weighting extends standard inverse probability weighting by incorporating safeguards against misspecification of the propensity score model and extreme weights. It combines techniques such as weight trimming, overlap weighting, or augmented outcome models to ensure that causal effect estimates remain reliable even when the propensity score model is imperfectly specified. | Sensitivity analysis for causality assesses how robust a causal conclusion is to unobserved confounding. Rather than assuming all confounders are controlled, it asks: how strong would an unmeasured variable need to be to overturn the estimated effect? It is an indispensable robustness check after any quasi-experimental or observational causal analysis. |
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