Extending a Rational Process Model of Causal Reasoning: Assessing Markov Violations and Explaining Away With Inhibitory Causal Relations

Causes sometimes decrease rather increase the probability of an effect, as when drinking coffee lowers the probability of sleep or an aspirin eliminates a headache. This research tests how two causal reasoning errors that have influenced the development of theories of human causal reasoning manifest themselves in the presence of inhibitory causal relations. Past research with generative causal relations (a cause makes its effect more probable) has shown that people violate the Markov condition, the pattern of independence that should obtain among causally related variables. And it has shown that they explain away—the phenomenon in which one should lower likelihood of one event when another is discovered to have occurred (e.g., exonerating one murder suspect when evidence against another is found)—too little or not at all. The new empirical findings reported here reveal that both sorts of errors manifest themselves when inhibitory causal relations are present although, unexpectedly, the direction of those errors sometimes reverses. Only the mutation sampler, a rational process model of human causal reasoning, correctly predicted these novel empirical findings. These results support the view that causal reasoning errors can be understood as arising from rational inference constrained by limited cognitive resources.