Agents and Causes:

Reconciling Competing Theories of Causal Reasoning 

Michael R. Waldmann and Ralf Mayrhofer

Currently in both psychology and philosophy two important frameworks of causal reasoning compete. Whereas dependency theories (e.g., causal Bayes nets) focus on causally motivated statistical or counterfactual dependencies between events, dispositional theories model causation as arising from the interaction between causal participants endowed with intrinsic dispositions or forces (e.g., force dynamics). The main goal of the present project is to reconcile these two competing frameworks. In a series of experiments we have focused on one of the most fundamental assumptions underlying causal Bayes nets, 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 and tested a causal Bayes net model which captures different error attributions using a hidden common preventive noise source that provides a rational explanation of the presence or absence of Markov violations.