The Causal Sampler: A Sampling Approach to Causal Representation, Reasoning, and Learning.
Zachary J. Davis, Bob Rehder · eScholarship (California Digital Library) · 2017
Although the causal graphical model framework has achievedsuccess accounting for numerous causal-based judgments, akey property of these models, the Markov condition, is con-sistently violated (Rehder, 2014; Rehder & Davis, 2016). Anew process model—the causal sampler—accounts for theseeffects in a psychologically plausible manner by assumingthat people construct their causal representations using theMetropolis-Hastings sampling algorithm constrained to onlya small number of samples (e.g., < 20). Because it assumesthat Markov violations are built into people’s causal represen-tations, the causal sampler accounts for the fact that those vio-lations manifest themselves in multiple tasks (both causal rea-soning and learning). This prediction was corroborated by anew experiment that directly measured people’s causal repre-sentations.