Assessing conditional causal effect via characteristic score

Abstract

Observational studies usually include participants representing the wide heterogeneous population. The conditional causal effect, treatment effect conditional on baseline characteristics, is of practical importance. Its estimation is subject to two challenges. First, the causal effect is not observable in any individual due to counterfactuality. Second, high-dimensional baseline variables are involved to satisfy the ignorable treatment selection assumption and to attain better estimation efficiency. In this work, a nonparametric estimation procedure, along with a pseudo-response, is proposed to estimate the conditional treatment effect through "characteristic score"-a parsimonious representation of baseline variable influence on treatment benefit. Adopting sparse dimension reduction with variable prescreening in the proposed estimation, we aim to identify the key baseline variables that impact the conditional treatment effect and to uncover the characteristic score that best predicts the treatment effect. This approach is applied to an HIV study for assessing the benefit of antiretroviral regimens and identifying the beneficiary subpopulation.

Keywords: causal inference; conditional causal effect; dimension reduction; high dimensionality; nonparametric regression; sparse dimension reduction.