标题： 扩展的条件独立性及其在因果推断中的应用 显示英文标题

标题： Extended Conditional Independence and Applications in Causal Inference

摘要： 本文的目的在于在一个更为广义的扩展条件独立性的概念下，整合随机条件独立性和变化条件独立性的思想。 我们证明了，在适当的假设下，分别适用于这两个情况的演算（分离元公理）仍然适用于扩展的情况。 这些结果为广泛的统计概念提供了严谨的基础，包括辅助性和充分性，并且特别为用于表达因果特性和进行因果推断的统计因果性决策理论框架奠定了基础，该框架使用条件独立性的语言和演算来表述因果属性并做出因果推断。 显示英文摘要

摘要： The goal of this paper is to integrate the notions of stochastic conditional independence and variation conditional independence under a more general notion of extended conditional independence. We show that under appropriate assumptions the calculus that applies for the two cases separately (axioms of a separoid) still applies for the extended case. These results provide a rigorous basis for a wide range of statistical concepts, including ancillarity and sufficiency, and, in particular, the Decision Theoretic framework for statistical causality, which uses the language and calculus of conditional independence in order to express causal properties and make causal inferences.