A Quantum Probability Approach to Human Causal Reasoning

When people make inferences about causal situations with vague and imperfect information, their judgments often deviate from the normative prescription of classical probability. As a result, it is difficult to apply popular models of causal reasoning such as ∆P and causal power, which provide good accounts of behavior in casual learning tasks and tasks where statistical information is provided directly. We propose a unified explanation of human causal reasoning using quantum probability theory that can account for causal reasoning across many different domains. In our approach, we postulate a hierarchy of mental representations, from fully quantum to fully classical, that could be adopted for different situations. We illustrate our approach with new experiments and model comparisons.