Individualized Prediction Bands in Causal Inference with Continuous Treatments

TL;DR

This paper introduces individualized prediction bands (IPB) for quantifying uncertainty in dose-response curves in causal inference with continuous treatments, addressing a gap in personalized medicine.

Contribution

The paper proposes a novel method, IPB, that leverages conformal prediction to provide individualized uncertainty quantification for continuous treatment effects.

Findings

IPB effectively quantifies individual dose-response uncertainty.

Simulation and real data demonstrate IPB's practical utility.

IPB reveals additional medical expenditure due to smoking for individuals.

Abstract

Individualized treatments are crucial for optimal decision making and treatment allocation, specifically in personalized medicine based on the estimation of an individual's dose-response curve across a continuum of treatment levels, e.g., drug dosage. Current works focus on conditional mean and median estimates, which are useful but do not provide the full picture. We propose viewing causal inference with a continuous treatment as a covariate shift. This allows us to leverage existing weighted conformal prediction methods with both quantile and point estimates to compute individualized uncertainty quantification for dose-response curves. Our method, individualized prediction bands (IPB), is demonstrated via simulations and a real data analysis, which demonstrates the additional medical expenditure caused by continued smoking for selected individuals. The results demonstrate that IPB provides an effective solution to a gap in individual dose-response uncertainty quantification literature.