Among Judea Pearl's many contributions to Causality and Statistics, the graphical d-separation} criterion, the do-calculus and the mediation formula stand out. In this chapter we show that d-separation} provides direct insight into an earlier causal model originally described in terms of potential outcomes and event trees. In turn, the resulting synthesis leads to a simplification of the do-calculus that clarifies and separates the underlying concepts, and a simple counterfactual formulation of a complete identification algorithm in causal models with hidden variables.
翻译:在Judea Pearl对因果关系和统计、图形 d - 分离标准、 do - calculus 和调解公式的许多贡献中,有不少突出之处。 在本章中,我们显示, d - 分离 } 直接揭示了最初以潜在结果和事件树描述的早期因果模型。 反过来,由此形成的合成简化了用于澄清和区分基本概念的“ do - calculus”和“ do - separation ”, 并简单反省了由隐藏变量组成的因果模型中完整的识别算法。