Effects of causal structure and evidential impact on probabilistic reasoning
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Effects of causal structure and evidential impact on probabilistic reasoning

Abstract

We compare two perspectives on base-rate neglect (Kahneman & Tversky, 1973) in probabilistic judgment. The evidential impact perspective derives it from humans' focus on the impact of evidence on belief, rather than conditional probabilities. The Causal Models perspective derives it from humans' inability to integrate information that is causally opaque, as base-rates often are in such experiments. Because causal and evidential-impact relations are often concomitant and confounded, we designed an experiment that specifically teases apart their respective influence on probabilistic judgment. Our results support a combination of the two perspectives, with causal transparency influencing the degree to which one engages in evidential impact reasoning strategies.

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