Causal Learning from Trending Time-Series

AbstractTwo studies investigated how people learn the strength of the relation between a cause and an effect in a time series setting in which both variables exhibit temporal trends. In prior research, we found that people control for temporal trends by focusing on transitions, how variables change from one observation to the next in a trial-by-trial presentation (Soo & Rottman, 2018). In Experiment 1, we replicated this effect, and found further evidence that people rely on transitions when there are extremely strong temporal trends. In Experiment 2, we investigated how people infer causal relations from time series data when presented as time series graphs. Though people were often able to control for the temporal trends, they had difficulty primarily when the cause and effect exhibited trends in opposite directions and there was a positive causal relationship. These findings shed light on when people can and can’t accurately learn causal relations in time-series settings.


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