Statistical and Mechanistic Information in Evaluating Causal Claims

Abstract

People use a variety of strategies for evaluating causal claims, including mechanistic strategies (seeking a step-by-step explanation for how a cause would bring about its effect) and statistical strategies (examining patterns of co-occurrence). Two studies examine factors leading one or the other of these strategies to predominate. First, general causal claims (e.g., "Smoking causes cancer") are evaluated predominantly using statistical evidence, whereas statistics is less preferred for specific claims (e.g., "Smoking caused Jack’s cancer"). Second, social and biological causal claims are evaluated primarily through statistical evidence, whereas statistical evidence is deemed less relevant for evaluating physical causal claims. We argue for a pluralistic view of causal learning on which a multiplicity of causal concepts lead to distinct strategies for learning about causation.


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