Conformal Diffusion Models for Individual Treatment Effect Estimation and Inference
This work addresses the challenge of personalized care by providing granular treatment effect estimates, though it appears incremental as it builds on existing methods like diffusion models and conformal inference.
The paper tackles the problem of estimating individual treatment effects from observational data by proposing a conformal diffusion model-based approach, which unbiasedly estimates potential outcome distributions and constructs informative confidence intervals with competitive performance demonstrated in numerical studies.
Estimating treatment effects from observational data is of central interest across numerous application domains. Individual treatment effect offers the most granular measure of treatment effect on an individual level, and is the most useful to facilitate personalized care. However, its estimation and inference remain underdeveloped due to several challenges. In this article, we propose a novel conformal diffusion model-based approach that addresses those intricate challenges. We integrate the highly flexible diffusion modeling, the model-free statistical inference paradigm of conformal inference, along with propensity score and covariate local approximation that tackle distributional shifts. We unbiasedly estimate the distributions of potential outcomes for individual treatment effect, construct an informative confidence interval, and establish rigorous theoretical guarantees. We demonstrate the competitive performance of the proposed method over existing solutions through extensive numerical studies.