When judging what caused an event, people do not treat all factors equally – for instance, they will say that a forest fire was caused by a lit match, and not mention the oxygen in the air which helped fuel the fire. We develop a computational model formalizing the idea that causal judgment is designed to identify “portable” causes - causes that are likely to generalize across a variety of background circumstances. Under minimal assumptions, the model is surprisingly simple: a factor is regarded as a cause of an outcome to the extent that it is, across counterfactual worlds, correlated with that outcome. The model explains why causal judgment is influenced by the normality of candidate causes, and outperforms other known computational models when tested against an existing fine-grained dataset of human graded causal judgments (Morris, A., Phillips, J., Gerstenberg, T., & Cushman, F. (2019). Quantitative causal selection patterns in token causation. PloS one, 14(8).).

在判断造成事件的原因时，人们不会平等地对待所有因素-例如，他们会说森林大火是由点燃的火柴引起的，更不用说空气中有助于燃烧的氧气了。我们开发了一个计算模型，正式确定了因果判断旨在识别“便携式”原因的想法，这些原因是可能在各种背景情况下普遍存在的原因。在最小的假设下，该模型非常简单：一个因素被认为是导致结果的原因，因为在整个反事实世界中，该因素都与该结果相关。该模型解释了因果判断为何受候选原因的正态性影响的原因，并且在针对现有的人类因果判断的细粒度数据集进行测试时，其表现优于其他已知的计算模型（Morris，A.，Phillips，J.，Gerstenberg，T.，＆Cushman，F.（2019年）。令牌因果关系中的定量因果选择模式。一，14（8）。）