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Causal Quartets: Different Ways to Attain the Same Average Treatment Effect

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  • Andrew Gelman
  • Jessica Hullman
  • Lauren Kennedy

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.

Suggested Citation

  • Andrew Gelman & Jessica Hullman & Lauren Kennedy, 2024. "Causal Quartets: Different Ways to Attain the Same Average Treatment Effect," The American Statistician, Taylor & Francis Journals, vol. 78(3), pages 267-272, July.
    • Handle: RePEc:taf:amstat:v:78:y:2024:i:3:p:267-272
    DOI: 10.1080/00031305.2023.2267597
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