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).

本研究解决了因果模型中的实际因果关系定义问题，通过推广Pearl的决定性因果模型，引入了一种新的非决定性因果模型定义。该定义不仅有助于因果发现的逻辑分析，还通过结构简化的概念增强了因果模型的表达能力，结果与之前的定义几乎一致，指出了因果关系的更深层次理解。

实际因果关系与非确定性因果模型