61: Conformal Inference of Individualized Treatment Rules under Distributional Shift

Music City Center 

Individualized treatment rule (ITR) is a stepping stone to precision medicine. Off policy prediction plays a central role in the evaluation of ITRs and other decision-making processes. We propose a conformal individualized off-policy prediction method under distributional shift. Individualized off-policy prediction provides contextual-based prediction of outcome under any given decision rule, enabling more informative interpretation than a population mean value of the policy. When conducting off-policy evaluation for samples in a target population using an experimental population, the two populations may have different covariate distributions that brings additional challenge. In this project, we develop contextualized off-policy prediction intervals under covariate shift using conformal prediction, which gives the decision maker a more reliable policy evaluation in terms of uncertainty quantification since it comes with coverage guarantees. The extension to using observed data from multiple sources is also considered where conditional outcome distribution shift may exist. The performance of this method is evaluated in simulations and a sepsis data application.

Precision medicine

Causal inference

Individualized treatment rules

Transfer learning

Federated learning

Conformal inference 

Health Policy Statistics Section