Human causal induction: A glimpse at the whole picture

In the present work, most relevant evidence in causal learning literature is reviewed and a general cognitive architecture based on the available corpus of experimental data is proposed. However, contrary to algorithms formulated in the Bayesian nets framework, such architecture is not assumed to optimise the usefulness of the available information in order to induce the underlying causal structure as a whole. Instead, human reasoners seem to rely heavily on local clues and previous knowledge to discriminate between spurious and truly causal covariations, and piece those relations together only when they are demanded to do so. Bayesian networks and AI algorithms for causal inference are nonetheless considered valuable toots to identify the main computational goals of causal induction processes and to define the problems any intelligent causal inference system must solve.