Regression model
因果发现算法 (PC, FCI, LiNGAM)
因果发现是一类算法，可以直接从观测数据中学习描述因果结构的定向无环图 (DAG)。基于约束的 PC 和 FCI 算法由 Spirtes, Glymour 和 Scheines (2000) 开发，而 Shimizu 等人 (2006) 的 LiNGAM 模型则利用线性非高斯结构来定向边。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, Prediction, and Search (2nd ed.). MIT Press. ISBN: 978-0262194402
- Shimizu, S., Hoyer, P. O., Hyvärinen, A., & Kerminen, A. (2006). A Linear Non-Gaussian Acyclic Model for Causal Discovery. Journal of Machine Learning Research, 7, 2003-2030. link ↗
如何引用本页
ScholarGate. (2026, June 1). Causal Discovery Algorithms (PC, FCI, LiNGAM). ScholarGate. https://scholargate.app/zh/causal-inference/causal-discovery
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- 因果识别（使用do演算）因果推断↔ compare
- 双重差分法 (Diff-in-Diff)计量经济学↔ compare
- 因果推断的工具变量（IV）方法卫生经济学↔ compare
- 普通最小二乘法 (OLS) 回归计量经济学↔ compare
- 倾向得分匹配研究统计学↔ compare