方法对比
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| 因果推断中的安慰剂检验× | 因果发现算法 (PC, FCI, LiNGAM)× | |
|---|---|---|
| 领域 | 因果推断 | 因果推断 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2010 | 2000 |
| 提出者≠ | Abadie, Diamond & Hainmueller (synthetic control placebos); Imbens & Lemieux (RDD validity) | Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM) |
| 类型≠ | Falsification / robustness test family for causal inference | Causal structure learning |
| 开创性文献≠ | Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program. Journal of the American Statistical Association, 105(490), 493-505. DOI ↗ | Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, Prediction, and Search (2nd ed.). MIT Press. ISBN: 978-0262194402 |
| 别名≠ | falsification tests, placebo checks, refutation tests, Plasebo Testleri — Nedensel Çıkarım Doğrulama | PC algorithm, FCI algorithm, LiNGAM, causal structure learning |
| 相关 | 5 | 5 |
| 摘要≠ | Placebo tests are a family of falsification checks that probe the credibility of a causal claim by re-running the analysis on a fake treatment, a false intervention date, or an outcome that should not have been affected. The approach was popularised through the synthetic control work of Abadie, Diamond and Hainmueller (2010) and the regression-discontinuity validity checks of Imbens and Lemieux (2008). | 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. |
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