Causal Markov violations and hidden mechanisms

Past studies have shown that while causal Bayes nets account for many of the causal inferences participants make in psychological experiments, persistent violations of one of their most fundamental axioms, the causal Markov condition, are found. Previous studies have attempted to account for such violations by conjecturing that participants posit causal representations that diverge from the causal representations intended by the experimenters. In this article, a novel method is presented for addressing the unsolved problem of how to determine which causal representations participants apply in a causal inference problem, and it is investigated how they update these causal representations when information about the underlying mechanism is given. The experiments thereby systematically investigate how participants represent causal structures and integrate mechanistic information to examine whether assumptions about hidden mechanisms stemming from these sources can account for their Markov violations. Through these experiments, it is found that participants' Markov violations can neither be accounted for through hidden variables that do not entail the conditional independence relations investigated nor through missing knowledge about underlying causal mechanisms. Instead, they indicate a persistent reasoning error, which is shown to be robust to variations in task formats but which can be reduced under certain circumstances. (PsycInfo Database Record (c) 2026 APA, all rights reserved).