  This paper introduces a collection of four data sets, similar to Anscombe's
Quartet, that aim to highlight the challenges involved when estimating causal
effects. Each of the four data sets is generated based on a distinct causal
mechanism: the first involves a collider, the second involves a confounder, the
third involves a mediator, and the fourth involves the induction of M-Bias by
an included factor. The paper includes a mathematical summary of each data set,
as well as directed acyclic graphs that depict the relationships between the
variables. Despite the fact that the statistical summaries and visualizations
for each data set are identical, the true causal effect differs, and estimating
it correctly requires knowledge of the data-generating mechanism. These example
data sets can help practitioners gain a better understanding of the assumptions
underlying causal inference methods and emphasize the importance of gathering
more information beyond what can be obtained from statistical tools alone. The
paper also includes R code for reproducing all figures and provides access to
the data sets themselves through an R package named quartets.
