A Magic Act in Causal Reasoning: Making Markov Violations Disappear

A desirable property of any theory of causal reasoning is to explain not only why people make causal reasoning errors but also <i>when</i> they make them. The <i>mutation sampler</i> is a rational process model of human causal reasoning that yields normatively correct inferences when sufficient cognitive resources are available but introduces systematic errors when they are not. The mutation sampler has been shown to account for a number of causal reasoning errors, including <i>Markov violations</i>, the phenomenon in which human reasoners treat causally related variables as statistically dependent when they are normatively independent. A Markov violation arises, for example, when an individual reasoning about a causal chain X→Y→Z treats <i>X</i> as informative about the state of <i>Z</i> even when the state of <i>Y</i> is known. Recently, the mutation sampler was used to predict the existence of previously untested experimental conditions in which the <i>sign</i> of Markov violations would switch from positive to negative. Here, it was used to predict the existence of conditions in which Markov violations should <i>disappear</i> entirely. In fact, asking subjects to reason about a novel causal structure with nothing but <i>generative</i> causal relations (a cause makes its effect more likely) resulted in Markov violations in the usual positive direction. But simply describing one of four causal relations as <i>inhibitory</i> (the cause makes its effect less likely) resulted in the elimination of those violations. Theoretical model fitting confirmed how this novel result is predicted by the mutation sampler.