Explanatory Power as a Substitute for Statistical Reasoning

People judge the strength of cause-and-effect relationships as a matter of routine, and often do so in the absence of evidence about the covariation between cause and effect. Here, we examine the possibility that explanatory power is used as a heuristic for making these judgments. Our argument proceeds in three steps. First, we show that explanatory power and causal strength judgments for sets of historical events are almost perfectly correlated (Study 1). Next, we intervene on explanatory power without changing the target causal relation by manipulating explanatory scope—the number of effects predicted by an explanation. Scope manipulations lead to downstream consequences for causal strength judgments (Study 2), supported by item-by-item correlations between causal strength and explanatory power (Study 3). Finally, we show that explanatory power has causal signatures even in the non-causal domain of mathematics (Study 4). These results suggest that explanatory power may be a useful heuristic for estimating causal strength in the absence of statistical evidence.