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| Đánh giá Chính sách bằng Khớp Chính xác Tinh giản (CEM)× | 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≠ | 2011-2012 | 2000 |
| Người khởi xướng≠ | Iacus, King & Porro | Robins, Hernán & Brumback |
| Loại≠ | Matching / quasi-experimental design | Causal inference weighting estimator |
| Công trình gốc≠ | Iacus, S. M., King, G., & Porro, G. (2012). Causal inference without balance checking: Coarsened exact matching. Political Analysis, 20(1), 1-24. 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≠ | CEM, Coarsened Exact Matching, CEM policy evaluation, coarsening-based matching | IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting |
| Liên quan | 5 | 5 |
| Tóm tắt≠ | Coarsened Exact Matching (CEM) is a quasi-experimental causal-inference technique that creates balanced treatment and control groups from observational data by temporarily coarsening covariates into bins, exactly matching units within those bins, and then pruning unmatched observations before estimating policy effects. Introduced by Iacus, King, and Porro, CEM belongs to the monotonic imbalance bounding family of matching methods and is especially popular in policy evaluation. | 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. |
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