So sánh phương pháp
Xem các phương pháp đã chọn cạnh nhau; những hàng khác biệt được làm nổi bật.
| Ghép Xu hướng Xuất hiện Bayes (Bayesian Propensity Score Matching)× | Trọng số Xác suất Nghịch đảo của Điều trị (IPW / IPTW)× | |
|---|---|---|
| Lĩnh vực | Suy luận nhân quả | Suy luận nhân quả |
| Họ | Regression model | Regression model |
| Năm ra đời≠ | 2012 | 2000 |
| Người khởi xướng≠ | Kaplan & Chen (2012); foundational PSM by Rosenbaum & Rubin (1983) | Robins, Hernán & Brumback |
| Loại≠ | Bayesian causal inference / matching | Causal inference weighting estimator |
| Công trình gốc≠ | Kaplan, D., & Chen, J. (2012). A Two-Step Bayesian Approach for Propensity Score Analysis: Simulations and Case Study. Psychometrika, 77(3), 581-609. DOI ↗ | Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560. DOI ↗ |
| Tên gọi khác≠ | Bayesian PSM, BPSM, Bayesian matching estimator, Bayesian propensity weighting | IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting |
| Liên quan≠ | 6 | 5 |
| Tóm tắt≠ | Bayesian Propensity Score Matching (Bayesian PSM) extends classical propensity score matching by placing a prior distribution over the propensity model parameters and propagating posterior uncertainty through the matching and outcome stages. Introduced formally by Kaplan and Chen (2012), it offers a principled account of estimation uncertainty that frequentist matching commonly ignores, and allows incorporation of substantive prior knowledge about treatment selection. | Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias. |
| ScholarGateBộ dữ liệu ↗ |
|
|