Individualized Prediction Bands in Causal Inference with Continuous Treatments

2025/11/19 by Sampson, Max, Chan, Kung-Sik
Mathematics · #Advanced Causal Inference Techniques #Applications (stat.AP) #FOS: Computer and information sciences #Methodology (stat.ME) #Statistical Methods and Bayesian Inference #Statistical Methods in Clinical Trials

paper · doi:10.48550/arxiv.2511.15075

openalex publication_date 2025/11/19 · openalex created_date 2025/11/23 · openalex updated_date 2026/07/28

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.

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