Abstract
The average causal effect can often be best understood in the context of its variation. We demonstrate with two sets of four graphs, all of which represent the same average effect but with much different patterns of heterogeneity. As with the famous correlation quartet of Anscombe, these graphs dramatize the way in which real-world variation can be more complex than simple numerical summaries. The graphs also give insight into why the average effect is often much smaller than anticipated.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 267-272 |
| Number of pages | 6 |
| Journal | American Statistician |
| Volume | 78 |
| Issue number | 3 |
| DOIs | |
| State | Published - 2024 |
Funding
We thank Dan Goldstein, Stephen Stigler, Howard Wainer, and two anonymous reviewers for helpful comments and the U.S. Office of Naval Research and Institute of Education Sciences for partial support of this work.
Keywords
- Causal inference
- Statistical graphics
- Variation
ASJC Scopus subject areas
- Statistics and Probability
- General Mathematics
- Statistics, Probability and Uncertainty