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.