用非确定性因果模型重新定义实际原因，更适用于复杂系统。
Actual Causation and Nondeterministic Causal Models
- 基于反事实依赖的扩展和结构简化构建新定义
- 新定义与旧版结论几乎一致，但更具普适性
- 适合研究因果推理与哲学逻辑的学者
在（Beckers, 2025）中，我提出了非确定性因果模型，作为Pearl标准确定性因果模型的推广。本文利用这些模型更高的表达能力，提出一种新的实际原因定义（同样适用于确定性模型）。不同于以往通过具体例子的主观直觉来论证，本文完全基于该定义在传递和学习因果模型中的唯一功能来推导。首先推广了基本的反事实依赖概念；其次展示该概念在因果发现逻辑中的关键作用；接着引入因果模型的结构简化概念；最后将两者结合，形成实际原因的新定义。尽管新颖，该定义得出的判断几乎与我之前的版本（Beckers, 2021, 2022）一致。
原文摘要 · Abstract (English)
In (Beckers, 2025) I introduced nondeterministic causal models as a generalization of Pearl's standard deterministic causal models. I here take advantage of the increased expressivity offered by these models to offer a novel definition of actual causation (that also applies to deterministic models). Instead of motivating the definition by way of (often subjective) intuitions about examples, I proceed by developing it based entirely on the unique function that it can fulfil in communicating and learning a causal model. First I generalize the more basic notion of counterfactual dependence, second I show how this notion has a vital role to play in the logic of causal discovery, third I introduce the notion of a structural simplification of a causal model, and lastly I bring both notions together in my definition of actual causation. Although novel, the resulting definition arrives at verdicts that are almost identical to those of my previous definition (Beckers, 2021, 2022).
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