# Causal Learning With Continuous Variables Over Time

- Kevin Soo,
*University of Pittsburgh, Pittsburgh, Pennsylvania, USA*
- Benjamin Rottman,
*University of Pittsburgh, Pittsburgh, Pennsylvania, USA*

## Abstract

When estimating the strength of the relation between a cause (X)
and effect (Y), there are two main statistical approaches that can be used. The
first is using a simple correlation. The second approach, appropriate for
situations in which the variables are observed unfolding over time, is to take a
correlation of the change scores – whether the variables reliably change in
the same or opposite direction. The main question of this manuscript is whether
lay people use change scores for assessing causal strength in time series
contexts. We found that subjects’ causal strength judgments were better
predicted by change scores than the simple correlation, and that use of change
scores was facilitated by naturalistic stimuli. Further, people use a heuristic
of simplifying the magnitudes of change scores into a binary code (increase vs.
decrease). These findings help explain how people uncover true causal relations
in complex time series contexts.

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