AI engineering glossary · Statistics inference
What is ATE (average treatment effect)?
Average treatment effect (also called ATE): The mean difference in outcome between receiving a treatment and not receiving it, averaged over a population: ATE = E[Y(1) - Y(0)]. Randomised experiments estimate it by comparing group averages.
Average treatment effect: an example
If treated users spend Rs 620 on average and control users Rs 590 in a randomised test, the estimated effect is Rs 30 per user.
Related terms
More Statistics inference terms
- Bayesian inference
- Benjamini-Hochberg procedure
- Bias of an estimator
- Bonferroni correction
- Bootstrap
- Causal graph
- Causal inference
- Chi-square goodness-of-fit test
Where you learn it
Average treatment effect is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.