The overlap-weighted average treatment effect averages the conditional average treatment effect with weight proportional to , where is the propensity score. It emphasizes covariate strata in which both treatments are plausible.
A heterogeneous treatment effect varies across units or covariate values rather than being constant throughout the population. The conditional average treatment effect describes one common form of this heterogeneity.
The average treatment effect on the treated is .
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The Average Treatment Effect (ATE) is a fundamental concept in causal inference and statistics that quantifies the effect of a treatment or intervention on an outcome of interest across a population. Specifically, ATE measures the average difference in outcomes between individuals who receive the treatment and those who do not.