一种基于刘易斯反事实的非干预式因果推理方法
A Non-Interventionist Approach to Causal Reasoning based on Lewisian Counterfactuals
AI总结：
本研究提出一种基于刘易斯反事实的非干预式因果推理语义，通过分解状态为命题赋值与因果基计算相似关系，无需干预即可形式化实际因果，还证明对应模型检测问题为PSPACE完全并给出QBF归约方法以支持自动验证。
摘要：
我们提出了一种具备计算基础的反事实条件句语义，其中：i）模型中的状态被分解为两个要素：命题赋值，以及以命题形式表示该状态下可用因果信息的因果基；ii）状态间的比较相似关系由状态的两个组成部分计算得出。我们证明，借助该语义，无需借助干预这一原始概念就能简洁地形式化实际因果的概念。此外，我们针对该语义下的反事实条件句语言，给出了模型检测问题的简洁表述。我们证明该问题是PSPACE完全的，并将其归约为QBF问题，可用于因果属性的自动验证。
英文摘要：
We present a computationally grounded semantics for counterfactual conditionals in which i) the state in a model is decomposed into two elements: a propositional valuation and a causal base in propositional form that represents the causal information available at the state; and ii) the comparative similarity relation between states is computed from the states' two components. We show that, by means of our semantics, we can elegantly formalize the notion of actual cause without recurring to the primitive notion of intervention. Furthermore, we provide a succinct formulation of the model checking problem for a language of counterfactual conditionals in our semantics. We show that this problem is PSPACE-complete and provide a reduction of it into QBF that can be used for automatic verification of causal properties.
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