Causal judgments about empirical information in an interrupted time series design.

Abstract

Empirical information available for causal judgment in everyday life tends to take the form of quasi-experimental designs, lacking control groups, more than the form of contingency information that is usually presented in experiments. Stimuli were presented in which values of an outcome variable for a single individual were recorded over six time periods, and an intervention was introduced between the fifth and sixth time periods. Participants judged whether and how much the intervention affected the outcome. With numerical stimulus information, judgments were higher for a pre-intervention profile in which all values were the same than for pre-intervention profiles with any other kind of trend. With graphical stimulus information, judgments were more sensitive to trends, tending to be higher when an increase after the intervention was preceded by a decreasing series than when it was preceded by an increasing series ending on the same value at the fifth time period. It is suggested that a feature-analytic model, in which the salience of different features of information varies between presentation formats, may provide the best prospect of explaining the results.