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The average causal effect can often be best understood in the context of itsvariation. We demonstrate with two sets of four graphs, all of which representthe same average effect but with much different patterns of heterogeneity. Aswith the famous correlation quartet of Anscombe (1973), these graphs dramatizethe way in which real-world variation can be more complex than simple numericalsummaries. The graphs also give insight into why the average effect is oftenmuch smaller than anticipated.

Causal quartets: Different ways to attain the same average treatment effect