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.

Causal selection is the process whereby people decide which of several events responsible for a realized outcome should be considered as “the cause” of that outcome. A theory of causal selection requires a definition of the relevant candidates to be considered for selection. So far, the psychological literature has operated on the implicit premise that the only relevant candidates for causal selection are individual variables, corresponding to the distinct nodes of a causal network. Instead, we argue that causal judgment can recognize plural causes, featuring more than one variable. We provide evidence for the psychological relevance of plural causes by showing: (a) that plural cause judgments are influenced by the same factors that have been proven to influence causal selection judgments in general; (b) that this influence cannot be explained away by assuming that participants estimate the strength of plural cause simply by combining the strength of its individual constituents.

Building on counterfactual theories of causal-selection, according to which humans intuitively evaluate the causal responsibility of events, we developed an experimental paradigm to examine the effect of causal-selection explanations on abductive causal inference. In our experiment, participants attempted to infer the rule responsible for winning outcomes of random draws from urns with varying sampling probabilities. Participants who were provided with causal-selection judgments as explanations for the outcomes made significantly closer inferences to the rule than those relying on observations alone, or on other explanations of causal relevance. We mirror these empirical results with a computational model of inference from explanation leveraging the theories of causal selection.