Improved Precision in Estimating Average Treatment Effects

Understand

The Average Treatment Effect (ATE) is a global measure of the effectiveness of an experimental treatment intervention.

• Classical methods of its estimation either ignore relevant covariates or do not fully exploit them.
• Moreover, past work has considered covariates as fixed.
• We present a method for improving the precision of the ATE estimate: the treatment and control responses are estimated via a regression, and information is pooled between the groups to produce an asymptotically unbiased estimate; we subsequently justify the random X paradigm underlying the result.