提出无需因果模型的因果判定方法，可处理复杂逻辑表达式。
Causality Without Causal Models
- 从因果模型中抽象出核心机制，适用于任意可定义反事实的模型。
- 能判定含析取、否定、信念等复杂公式的因果关系，突破原有框架限制。
- 适用于非因果模型场景，适合形式化推理与理论研究者使用。
目前最主流的（实际）因果定义源自Halpern和Pearl，基于因果模型（又称结构方程模型）。本文对这一定义进行抽象，提取其关键特征，使其可应用于任意能定义反事实的模型。通过抽象，不仅可将该定义拓展至更广泛的模型，包括允许回溯的模型，还能在无需因果模型的前提下判断公式间是否存在因果关系，如涉及析取、否定、信念及嵌套反事实等情形（这些均无法被原定义处理）。此外，该思想还可延伸至解释的抽象定义，超越因果模型范畴。最终，我们对原始定义中的特性有了更深入理解。
原文摘要 · Abstract (English)
Perhaps the most prominent current definition of (actual) causality is due to Halpern and Pearl. It is defined using causal models (also known as structural equations models). We abstract the definition, extracting its key features, so that it can be applied to any other model where counterfactuals are defined. By abstracting the definition, we gain a number of benefits. Not only can we apply the definition in a wider range of models, including ones that allow, for example, backtracking, but we can apply the definition to determine if A is a cause of B even if A and B are formulas involving disjunctions, negations, beliefs, and nested counterfactuals (none of which can be handled by the Halpern-Pearl definition). Moreover, we can extend the ideas to getting an abstract definition of explanation that can be applied beyond causal models. Finally, we gain a deeper understanding of features of the definition even in causal models.
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