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| Nhận dạng nhân quả với Đồ thị có hướng không chu trình (do-calculus)× | Thiết kế Gián đoạn Hồi quy (Regression Discontinuity Design - RDD)× | Biến công cụ thông qua Bình phương tối thiểu hai giai đoạn (IV/2SLS)× | |
|---|---|---|---|
| Lĩnh vực | Suy luận nhân quả | Suy luận nhân quả | Suy luận nhân quả |
| Họ | Regression model | Regression model | Regression model |
| Năm ra đời≠ | 2009 | 2008 | 2009 |
| Người khởi xướng≠ | Judea Pearl | Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction) | Angrist & Pischke (textbook treatment); Stock & Yogo (weak-instrument theory) |
| Loại≠ | Causal identification framework | Quasi-experimental causal design | Instrumental-variables regression |
| Công trình gốc≠ | Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press. ISBN: 978-0521895606 | Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635. DOI ↗ | Angrist, J. D. & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355 |
| Tên gọi khác≠ | do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus) | RDD, regression discontinuity design, sharp RDD, fuzzy RDD | instrumental variables, IV estimation, 2SLS, instrumental variable regression |
| Liên quan | 5 | 5 | 5 |
| Tóm tắt≠ | DAG causal identification is a framework, developed by Judea Pearl (2009), that encodes causal assumptions as a directed acyclic graph and uses the do-calculus rules to determine whether and how a causal effect can be identified from observational data. It systematically handles confounders, instrumental variables, and backdoor paths. | 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. | IV/2SLS is a two-stage estimation method that recovers the causal effect of an endogenous regressor by isolating the part of its variation driven by an external instrument. It is the workhorse identification strategy in modern applied econometrics, developed at length in Angrist and Pischke's Mostly Harmless Econometrics (2009). |
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