Elemental Causal Learning From Transitions

Much research on elemental causal learning has focused on how causal strength is learned from the states of variables. In longitudinal contexts, the way a cause and effect change over time can be informative of the underlying causal relationship. We propose a framework for inferring the causal strength from different observed transitions, and compare the predictions to existing models of causal induction. According to this framework, transitions where the cause and effect change simultaneously are the most informative about the underlying causal strength. The predictions of this framework are tested in an experiment where subjects observe a cause and effect over time, updating their judgments of causal strength after observing different transitions. The results are largely consistent with the proposed framework, showing that causal learning in longitudinal contexts relies on patterns of transitions – a previously overlooked source of information from which causal strength can be learned