  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.
