On this weeks episode of Casual Inference we talk about a “Causal Quartet” a set of four datasets generated under different mechanisms, all with the same statistical summaries (including visualizations!) but different true causal effects. The figures and tables are from our recent preprint: https://arxiv.org/pdf/2304.02683.pdf Given a single dataset with 3 variables: exposure, outcome and covariate (z) how can statistics help you decide whether to adjust for z? It can’t! For example her...
