  The average treatment effect (ATE) is popularly used to assess the treatment
effect. However, the ATE implicitly assumes a homogenous treatment effect even
amongst individuals with different characteristics. In this paper, we mainly
focus on assessing the treatment effect heterogeneity, which has important
implications in designing the optimal individual treatment regimens and in
policy making. The treatment benefit rate (TBR) and treatment harm rate (THR)
have been defined to characterize the magnitude of heterogeneity for binary
outcomes. When the outcomes are continuous, we extend the definitions of the
TBR and THR to compare the difference between potential outcomes with a
pre-specified level c. Unlike the ATE, these rates involve the joint
distribution of the potential outcomes and can not be identified without
further assumptions even in randomized clinical trials. In this article, we
assume the potential outcomes are independent conditional on the observed
covariates and an unmeasured latent variable. Under this assumption, we prove
the identification of the TBR and THR in non-separable (generalized) linear
models for both continuous and binary outcomes. We then propose estimators and
derive their asymptotic distributions. In the simulation studies, we implement
our proposed methods to assess the performance of our estimators and carry out
a sensitive analysis for different underlying distribution for the latent
variable. Finally, we illustrate the proposed methods in two randomized
controlled trials.
