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| Phân tích Trung gian Nhân quả (Hiệu ứng Trực tiếp và Gián tiếp Tự nhiên)× | Thiết kế Gián đoạn Hồi quy (Regression Discontinuity Design - RDD)× | |
|---|---|---|
| Lĩnh vực | Suy luận nhân quả | Suy luận nhân quả |
| Họ | Regression model | Regression model |
| Năm ra đời≠ | 2010 | 2008 |
| Người khởi xướng≠ | Pearl (2001); general framework by Imai, Keele & Tingley (2010) | Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction) |
| Loại≠ | Counterfactual causal decomposition | Quasi-experimental causal design |
| Công trình gốc≠ | Pearl, J. (2001). Direct and Indirect Effects. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI), 411-420. link ↗ | Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635. DOI ↗ |
| Tên gọi khác | natural direct effect, natural indirect effect, NDE / NIE decomposition, counterfactual mediation | RDD, regression discontinuity design, sharp RDD, fuzzy RDD |
| Liên quan | 5 | 5 |
| Tóm tắt≠ | Causal mediation analysis is a counterfactual framework that splits a treatment's total effect into a Natural Direct Effect (NDE) and a Natural Indirect Effect (NIE) that runs through a mediator. The modern general approach was formalised by Pearl (2001) and Imai, Keele and Tingley (2010), giving the decomposition a precise causal interpretation. | Regression Discontinuity Design is a quasi-experimental method that identifies a causal effect by locally comparing units just above and just below a cutoff on a continuous assignment (running) variable. Formalised for applied work by Imbens and Lemieux (2008) and developed as a practical framework by Cattaneo, Idrobo, and Titiunik (2020), it estimates a local average treatment effect (LATE) at the threshold. |
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