Agents and Causes: A Bayesian Error Attribution Model of Causal Reasoning


One of the most fundamental assumptions underlying causal Bayes nets is the Markov constraint. According to this constraint, an inference between a cause and an effect should be invariant across conditions in which other effects of this cause are present or absent. Previous research has demonstrated that reasoners tend to violate this assumption systematically over a wide range of domains. We hypothesize that people are guided by abstract assumptions about the mechanisms underlying otherwise identical causal relations. In particular, we suspect that the distinction between agents and patients, which can be disentangled from the distinction between causes and effects, influences which causal variable people blame when an error occurs. We have developed a causal Bayes net model which captures different error attributions using a hidden common preventive noise source that provides a rational explanation of these apparent violations. Experiments will be presented which confirm predictions derived from the model.

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