The Causal Sampler: A Sampling Approach to Causal Representation, Reasoning, and Learning

Although the causal graphical model framework has achieved success accounting for numerous causal-based judgments, a key property of these models, the Markov condition, is consistently violated (Rehder, 2014; Rehder & Davis, 2016). A new process model—the causal sampler—accounts for these effects in a psychologically plausible manner by assuming that people construct their causal representations using the Metropolis-Hastings sampling algorithm constrained to only a small number of samples (e.g., < 20). Because it assumes that Markov violations are built into people’s causal representations, the causal sampler accounts for the fact that those violations manifest themselves in multiple tasks (both causal reasoning and learning). This prediction was corroborated by a new experiment that directly measured people’s causal representations.