方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 因果发现算法 (PC, FCI, LiNGAM)× | 因果推断的工具变量（IV）方法× | 普通最小二乘法 (OLS) 回归× | |
|---|---|---|---|
| 领域≠ | 因果推断 | 卫生经济学 | 计量经济学 |
| 方法族≠ | Regression model | Process / pipeline | Regression model |
| 起源年份≠ | 2000 | 1990s (modern applications) | 2019 |
| 提出者≠ | Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM) | Angrist & Pischke (applied econometrics); rooted in econometric theory | Wooldridge (textbook treatment); classical least squares |
| 类型≠ | Causal structure learning | Method | Linear regression |
| 开创性文献≠ | Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, Prediction, and Search (2nd ed.). MIT Press. ISBN: 978-0262194402 | Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press. link ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| 别名≠ | PC algorithm, FCI algorithm, LiNGAM, causal structure learning | IV, two-stage least squares, TSLS, causal estimation | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| 相关≠ | 5 | 3 | 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. | Instrumental variables (IV) is an econometric method to estimate causal effects when treatment or exposure is not randomly assigned and confounding is severe or unmeasured. IV relies on a third variable (instrument) that influences treatment but does not directly affect the outcome, allowing researchers to isolate the causal effect from the noise of confounding. Developed extensively in econometrics (Angrist & Pischke, 1990s–2000s), IV methods are increasingly used in health economics and health services research to leverage natural experiments and policy changes. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
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