Causal Inference Is Not Just a Statistics Problem

Tuesday, February 20th, 20244:00 pm – 4:30 pm ET

Presented by: Lucy D'Agostino McGowan (Wake Forest University), Travis Gerke (The Prostate Cancer Clinical Trials Consortium), and Malcolm Barrett (Stanford University)


In this February edition of the JSDSE/CAUSE webinar series, we highlight the 2024 article: Causal Inference Is Not Just a Statistics Problem. The authors will discuss four datasets, similar to Anscombe’s quartet, that aim to highlight the challenges involved when estimating causal effects. Each of the four datasets 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. Despite the fact that the statistical summaries and visualizations for each dataset are identical, the true causal effect differs, and estimating it correctly requires knowledge of the data-generating mechanism. These example datasets 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.