发表机构
University of Inland Norway（内陆挪威大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出价值因果马尔可夫条件，发展因果价值理论，引入概率-价值对偶性，制定v-CMC不同版本并证明等价性，定义v-分离，推导贝尔曼型递归，还展示其对效用信息的作用并开发相关算法。
AI 中文摘要
本文提出了价值的因果独立性原则——价值因果马尔可夫条件（v-CMC），并发展了将因果关系与效用联系起来的“因果价值理论”的概念和数学基础。在推动v-CMC的局部表述后，引入概率-价值对偶性，将标准因果推理结果转化到价值设定中。具体制定了v-CMC的局部、全局和分解版本并证明其等价性，定义了v-分离并证明其对条件价值独立性是合理且完备的。推导出贝尔曼型递归作为v-CMC的特殊情况，将标准贝尔曼递归从线性链推广到因果有向无环图。展示了v-CMC如何支持跨因果上下文的效用信息的模块化转移和更新，并开发了用于因果结构效用引出和规范影响图构建的算法。
英文摘要
This paper proposes a causal independence principle for value -- the value Causal Markov Condition (v-CMC) -- and develops the conceptual and mathematical foundations of a "causal value theory" linking causality and utility. After motivating a local formulation of the v-CMC, we introduce a probability-value duality that translates standard causal-inference results into the value setting. In particular, we formulate local, global, and decomposition versions of the v-CMC and prove their equivalence. We also define v-separation and show that it is sound and complete for conditional value independence. Furthermore, we derive a Bellman-type recursion as a special case of the v-CMC, thereby generalizing standard Bellman recursion from linear chains to causal DAGs. Finally, we show how the v-CMC supports modular transfer and updating of utility information across causal contexts and develop algorithms for causally structured utility elicitation and canonical influence-diagram construction.
CommentsAccepted at UAI 2026
