Why scatter plots suggest causality, and what we can do about it

Scatter plots carry an implicit if subtle message about causality. Whether we
look at functions of one variable in pure mathematics, plots of experimental
measurements as a function of the experimental conditions, or scatter plots of
predictor and response variables, the value plotted on the vertical axis is by
convention assumed to be determined or influenced by the value on the
horizontal axis. This is a problem for the public understanding of scientific
results and perhaps also for professional scientists' interpretations of
scatter plots. To avoid suggesting a causal relationship between the x and y
values in a scatter plot, we propose a new type of data visualization, the
diamond plot. Diamond plots are essentially 45 degree rotations of ordinary
scatter plots; by visually jarring the viewer they clearly indicate that she
should not draw the usual distinction between independent/predictor variable
and dependent/response variable. Instead, she should see the relationship as
purely correlative.