Primary Submission Category: Machine Learning and Causal Inference
On the Role of Surrogates in Conformal Inference of Individual Causal Effects
Authors: Larry Han, Chenyin Gao, Peter Gilbert,
Presenting Author: Larry Han*
Learning the Individual Treatment Effect (ITE) is essential for personalized decision-making, yet causal inference has traditionally focused on aggregated treatment effects. While integrating conformal prediction with causal inference can provide valid uncertainty quantification for ITEs, the resulting prediction intervals are often excessively wide, limiting their practical utility. To address this limitation, we introduce Surrogate-assisted Conformal Inference for Efficient INdividual Causal Effects (SCIENCE), a framework designed to construct more efficient prediction intervals for ITEs. SCIENCE accommodates covariate shifts between source data containing primary outcomes, and target data containing only surrogate outcomes or covariates. Leveraging semi-parametric efficiency theory, SCIENCE produces rate double-robust prediction intervals under mild rate convergence conditions, permitting the use of flexible non-parametric models to estimate nuisance functions. We quantify efficiency gains by comparing semi-parametric efficiency bounds with and without the incorporation of surrogates. Simulation studies demonstrate that our surrogate-assisted intervals offer substantial efficiency improvements over existing methods while maintaining valid group-conditional coverage. Applied to the phase 3 Moderna COVID-19 vaccine trial, SCIENCE illustrates how multiple surrogates can be leveraged to generate more efficient prediction intervals.