Average treatment effect
Also known as: Average causal effect
The average causal effect of a treatment across everyone in the population.
What it means
The average treatment effect is the mean difference in outcomes between a population receiving a treatment and that same population not receiving it — the average of every unit's individual causal effect. Because both outcomes can never be observed for the same person, the ATE is estimated by contrasting groups made comparable by design: randomization makes the control group's average outcome a credible stand-in for the treated group's missing counterfactual, so a simple difference in means identifies it. It is the headline number in most trials, A/B tests, and policy evaluations. Its limitation is that an average conceals variation: a modest positive ATE can combine real gains for some people with harm for others, and under imperfect compliance or instrumental designs the estimate recovers a narrower quantity such as the local average treatment effect. It matters because the ATE is usually what 'does this work?' is taken to mean.
Examples
A retailer randomizes a new checkout layout to half of its visitors; the gap in average basket size between the two halves is the estimated average treatment effect.
A trial of a smoking-cessation app compares average quit rates across the assigned groups; the difference is the ATE, even though heavy smokers may gain far more than light ones.
A school district randomizes free breakfast to half its schools and reports the district-wide average change in attendance — one ATE that can hide schools where the effect was near zero.
First described in Neyman (1923); formalized by Donald Rubin (1974).