Humans are fundamentally primed for making causal attributions based on correlations. This implies that researchers
must be careful to present their results in a manner that inhibits unwarranted causal attribution. In this paper, we present
the results of an experiment that suggests regression models – one of the primary vehicles for analyzing statistical results
in political science – encourage causal interpretation. Specifically, we demonstrate that presenting observational results
in a regression model, rather than as a simple comparison of means, makes causal interpretation of the results more
likely. Our experiment drew on a sample of 235 university students from three different social science degree programs
(political science, sociology and economics), all of whom had received substantial training in statistics. The subjects were
asked to compare and evaluate the validity of equivalent results presented as either regression models or as a test
of two sample means. Our experiment shows that the subjects who were presented with results as estimates from a
regression model were more inclined to interpret these results causally. Our experiment implies that scholars using
regression models should note carefully both their models’ identifying assumptions and which causal attributions can
safely be concluded from their analysis.