The influence of temporal distributions on causal induction from tabular data

Two experiments investigated the joint influence of statistical and temporal information on causal inference from tabular data. Participants were presented with unambiguous data sets containing information about relative effect frequencies in cause-present and cause-absent situations. In addition to contingency information, the stimuli also revealed information about the temporal distribution of effects. The participants took this information into account when making causal judgments, so that the mere advancing or postponing of the effect in time was attached with causal significance, even when the cause did not increase the overall probability of the effect. These results cannot be reconciled with standard contingency accounts of causal induction.