方法对比
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| 因果发现算法 (PC, FCI, LiNGAM)× | 回归断点设计 (Regression Discontinuity Design, RDD)× | 工具变量法/两阶段最小二乘法 (IV/2SLS)× | |
|---|---|---|---|
| 领域 | 因果推断 | 因果推断 | 因果推断 |
| 方法族 | Regression model | Regression model | Regression model |
| 起源年份≠ | 2000 | 2008 | 2009 |
| 提出者≠ | Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM) | Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction) | Angrist & Pischke (textbook treatment); Stock & Yogo (weak-instrument theory) |
| 类型≠ | Causal structure learning | Quasi-experimental causal design | Instrumental-variables regression |
| 开创性文献≠ | Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, Prediction, and Search (2nd ed.). MIT Press. ISBN: 978-0262194402 | 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 |
| 别名≠ | PC algorithm, FCI algorithm, LiNGAM, causal structure learning | RDD, regression discontinuity design, sharp RDD, fuzzy RDD | instrumental variables, IV estimation, 2SLS, instrumental variable regression |
| 相关 | 5 | 5 | 5 |
| 摘要≠ | Causal discovery is a family of algorithms that automatically learn a directed acyclic graph (DAG) describing causal structure directly from observational data. The constraint-based PC and FCI algorithms were developed by Spirtes, Glymour and Scheines (2000), while the LiNGAM model of Shimizu et al. (2006) exploits linear non-Gaussian structure to orient edges. | 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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