A counterfactual simulation model of causal judgment

Abstract

How do people make causal judgments? We propose the counterfactual simulation model (CSM) of causal judgment which predicts causal judgments by comparing what actually happened with counterfactual simulations of what would have happened in relevant contingencies. It postulates different aspects of causation that capture whether the cause made a difference to whether and how the outcome occurred, and if the cause was sufficient and robust. We test the CSM in three experiments that ask participants to make judgments about dynamic collision events. Experiment 1 reveals a very close quantitative mapping between causal judgments, and participants’ belief that the outcome would have been different without the cause. Experiment 2 demonstrates that counterfactual contrasts are necessary for explaining causal judgments. Participants’ judgments differ dramatically between pairs of situations in which what actually happened was identical, but what would have happened differed. Experiment 3 features two candidate causes and shows that participants’ judgments are sensitive to different aspects of causation. The CSM provides a better fit to participants’ judgments than a heuristic model which uses features based on what actually happened. We discuss how the CSM can be used to model the semantics of different causal verbs, deal with causation by omission, and be extended beyond the physical domain.

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