Transitive reasoning distorts induction in causal chains

Abstract A probabilistic causal chain A→B→C may intui-tively appear to be transitive: If A probabilistically causes B, and B probabilistically causes C, A probabilistically causes C. However, probabilistic causal relations can only guaranteed to be transitive if the so-called Markov condition holds. In two experiments, we examined how people make probabilistic judgments about indirect relationships A→C in causal chains A→B→C that violate theMarkov condition.We hypothesized that participants would make transitive inferences in accor-dance with the Markov condition although they were present-ed with counterevidence showing intransitive data. For in-stance, participants were successively presented with data entailing positive dependencies A→B and B→C. At the same time, the data entailed that A and C were statistically indepen-dent. The results of two experiments show that transitive reason-ing via a mediating event B influenced and distorted the induc-tion of the indirect relation between A and C. Participants ’ judg-ments were affected by an interaction of transitive, causal-model-based inferences and the observed data. Our findings support the idea that people tend to chain individual causal relations into mental causal chains that obey the Markov condi-tion and thus allow for transitive reasoning, even if the observed data entail that such inferences are not warranted