Causal quartets: Different ways to attain the same average treatment effect
Methodology
2023-02-28 v1 Applications
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 (1973), 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.
Cite
@article{arxiv.2302.12878,
title = {Causal quartets: Different ways to attain the same average treatment effect},
author = {Andrew Gelman and Jessica Hullman and Lauren Kennedy},
journal= {arXiv preprint arXiv:2302.12878},
year = {2023}
}
Comments
12 pages, 3 figures