提出新反事实概率语义，适用于传统框架外的因果模型。
Causal Counterfactuals Reconsidered
- 基于满足马尔可夫条件的因果模型，不依赖虚构响应变量。
- 在非典型因果场景下仍可定义反事实概率，突破皮尔逊框架限制。
- 适合关注反事实推理理论基础的研究者，尤其关心因果建模哲学问题者。
本文提出一种反事实概率的新语义，可推广至无法扩展为真实结构因果模型的概率因果模型，这类模型即使在简单场景中也会出现。该语义既拒绝戴维德主张的普遍因果决定论和不现实变量，又支持皮尔逊认为反事实可普遍形式化之观点。模型仅包含现实变量、满足马尔可夫条件且因果完备。虽以结构因果模型为形式基础，但避免使用响应变量。证明该语义与两项无需结构模型的近期提案等价，并与文献中关于随机反事实的讨论一致。同时反思马尔可夫条件的普适性，提出因果抽象的新泛化形式。
原文摘要 · Abstract (English)
I develop a novel semantics for probabilities of counterfactuals that generalizes the standard Pearlian semantics: it applies to probabilistic causal models that cannot be extended into realistic structural causal models and are therefore beyond the scope of Pearl's semantics. This generalization is needed because, as I show, such probabilistic causal models arise even in simple settings. My semantics offer a natural compromize in the long-standing debate between Pearl and Dawid over counterfactuals: I agree with Dawid that universal causal determinism and unrealistic variables should be rejected, but I agree with Pearl that a general semantics of counterfactuals is nonetheless possible. I restrict attention to causal models that satisfy the Markov condition, only contain realistic variables, and are causally complete. Although I formulate my proposal using structural causal models, as does Pearl, I refrain from using so-called response variables. Moreover, I prove that my semantics is equivalent to two other recent proposals that do not involve structural causal models, and that it is in line with various comments on stochastic counterfactuals that have appeared in the literature more broadly. Throughout I also reflect on the universality of the Markov condition and explore a novel generalization of causal abstractions
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