论文标题
多元反事实系统和因果图形模型
Multivariate Counterfactual Systems And Causal Graphical Models
论文作者
论文摘要
在犹太人对因果关系和统计的许多贡献中，图形D分隔}标准，DO-Calculus和调解公式脱颖而出。在本章中，我们表明d-eparparation}直接洞悉了最初用潜在结果和事件树来描述的早期因果模型。反过来，所得的合成导致简化了DO-Calculus，从而阐明和分离了基本概念，并在具有隐藏变量的因果模型中对完整识别算法的简单反事实表述。
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.
