在部分已知因果图下，高效识别条件因果效应。
Identifying Conditional Causal Effects in MPDAGs
- 基于未受处理影响的调节集，给出可识别公式。
- 将经典反事实演算推广至MPDAG场景。
- 提出完整算法，适用于各类条件因果推断。
当因果图仅以最大定向部分有向无环图（MPDAG）形式已知时，我们研究条件因果效应的可识别性问题。MPDAG 表示由背景知识限制的一组等价图，且所有变量均可观测。本文提出三项成果：当调节集不受处理影响时，给出一个可识别公式；将经典的 do 计算规则推广至 MPDAG 设置；设计一种完备的算法，用于识别此类条件因果效应。
原文摘要 · Abstract (English)
We consider identifying a conditional causal effect when a graph is known up to a maximally oriented partially directed acyclic graph (MPDAG). An MPDAG represents an equivalence class of graphs that is restricted by background knowledge and where all variables in the causal model are observed. We provide three results that address identification in this setting: an identification formula when the conditioning set is unaffected by treatment, a generalization of the well-known do calculus to the MPDAG setting, and an algorithm that is complete for identifying these conditional effects.
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