Evidence from the future.

Abstract

The outcome of any scientific experiment or intervention will naturally unfold over time. How then should individuals make causal inferences from measurements over time? Across three experiments, we had participants observe experimental and control groups over several days posttreatment in a fictional biological research setting. We identify competing perspectives in the literature: contingency-driven accounts predict no effect of the outcome timing while the contiguity principle suggests people will view a treatment as more harmful to the extent that bad treatment outcomes occur earlier rather than later. In contrast, inference of the functional form of a treatment effect can license extrapolation beyond the measurements and lead to different causal inferences. We find participants' causal strength and direction judgments in temporal settings vary with minimal manipulations of instruction framing. When it is implied that the observations are made over a preplanned number of days, causal judgments depend strongly on contiguity. When it is implied that the observation may be ongoing, participants extrapolate current trends into the future and adapt their causal judgments accordingly. When data are revealed sequentially, participants rely on extrapolation regardless of instruction framing. Our results demonstrate human flexibility in interpreting temporal evidence for causal reasoning and emphasize human tendency to generalize from evidence in ways that are acutely sensitive to task framing. Public Significance StatementEveryday decision making is shaped by our judgments about how the world works, such as whether a new vaccine is beneficial or harmful for our health. Since more evidence is arriving all the time, we inevitably have to make these judgments with incomplete information. Here we show for the first time that people may use the timing of what has already happened to predict what will happen next, and then incorporate this predicted future evidence into their causal judgments. For instance, we find settings in which people take a rising trend to indicate that greater problems are to come, or a falling trend to indicate that the peak of a causal influence has already passed, leading them to make dramatically different causal judgments from the same overall case statistics. These results have implications both for understanding the sophistication of individual human causal reasoning and for pinpointing the source of disagreements in public discourse around scientific evidence.