A Transitivity Heuristic of Probabilistic Causal Reasoning

Abstract

In deterministic causal chains the relations ‘A causes B’ and ‘B causes C’ imply that ‘A causes C’. However, this is not necessarily the case for probabilistic causal relationships: A may probabilistically cause B, and B may probabilistically cause C, but A does not probabilistically cause C, but rather ¬C. The normal transitive inference is only valid when the Markov condition holds, a key feature of the Bayes net formalism. However, it has been objected that the Markov assumption does not need to hold in the real world. In our studies we examined how people reason about causal chains that do not obey the Markov condition. Three experiments involving causal reasoning within causal chains provide evidence that transitive reasoning seems to hold psychologically, even when it is objectively not valid. Whereas related research has shown that learners assume the Markov condition in causal chains in the absence of contradictory data, we here demonstrate the use of this assumption for situations in which participants were directly confronted with evidence contradicting the Markov condition. The results suggest a causal transitivity heuristic resulting from chaining individual causal links into mental causal models that obey the Markov condition.


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