When and how do people reason about unobserved causes?
When and how do people reason about unobserved causes?
Assumptions and beliefs about unobserved causes are critical for inferring causal relationships from observed correlations. For example, an unobserved factor can influence two observed variables, creating a spurious relationship. Or an observed cause may interact with unobserved factors to produce an effect, in which case the contingency between the observed cause and effect cannot be taken at face value to infer causality. This chapter reviews evidence that three types of situations lead people to infer unobserved causes: after observing single events that occur in the absence of any precipitating causal event, after observing a systematic pattern among events that cannot be explained by observed causes, and after observing a previously stable causal relationship change. In all three scenarios people make sophisticated inferences about unobserved causes to explain the observed data. This chapter discusses working memory as a requirement for reasoning about unobserved causes and briefly discuss implications for models of human causal reasoning.
Keywords: unobserved causes, causal learning, causal inference
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