Regression model
因果识别（使用do演算）
因果识别（使用do演算）是一个框架，由Judea Pearl（2009）开发，它将因果假设编码为有向无环图（DAG），并使用do演算规则来确定是否以及如何从观测数据中识别因果效应。该框架系统地处理混淆变量、工具变量和后门路径。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press. ISBN: 978-0521895606
- Pearl, J., Glymour, M., & Jewell, N. P. (2016). Causal Inference in Statistics: A Primer. Wiley. ISBN: 978-1119186847
如何引用本页
ScholarGate. (2026, June 1). Causal Identification with Directed Acyclic Graphs (do-calculus). ScholarGate. https://scholargate.app/zh/causal-inference/dag-identification
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.
- 因果推断的工具变量（IV）方法卫生经济学↔ compare
- 逆概率治疗加权法 (IPW / IPTW)因果推断↔ compare
- 中介分析统计学↔ compare
- 倾向得分匹配研究统计学↔ compare
- 对隐藏偏差的敏感性分析（Rosenbaum 界 / E 值）因果推断↔ compare