Categories → Causal Inference , Study Design , Causal Effect
In statistics, the average treatment effect (ATE) is a measure used to estimate the causal effect of a treatment or intervention on an outcome.
ATE stands for Average Treatment Effect.
It represents the average difference in the outcome variable between a group of subjects who received the treatment and a group of subjects who did not receive the treatment.
The ATE is one of many Causal Effects (aka Treatment Effects) you might want to estimate, and it is the most commonly used. Other causal effects consider the population affected by the treatment, and other factors which might otherwise bias the effect estimate.
ATE is an important concept because it allows us to estimate the causal effect of a treatment on an outcome in a population. It is often estimated using observational data when it is not feasible or ethical to conduct a randomized controlled trial (RCT).
To estimate the ATE, we first need to define the treatment and the outcome variable. Then, we compare the outcome variable between two groups: the group that received the treatment (the treatment group) and the group that did not receive the treatment (the control group). The ATE is calculated as the difference between the average outcome in the treatment group and the average outcome in the control group.
Mathematically, it can be represented as:
ATE = E[Y(1)] - E[Y(0)]
where Y(1) is the outcome variable for the treatment group, Y(0) is the outcome variable for the control group, and E[ ] denotes the expected value.
In summary, ATE is a popular statistical measure used to estimate the causal effect of a treatment or intervention on an outcome, and it is essential in causal inference to determine the effectiveness of a treatment or intervention.
We recommend this video if you want to find out more about the ATE: