Bayesian Neural Controlled Differential Equations for Treatment Effect Estimation

arXiv:2310.17463v2 Announce Type: replace
Abstract: Treatment effect estimation in continuous time is crucial for personalized medicine. However, existing methods for this task are limited to point estimates of the potential outcomes, whereas uncertainty estimates have been ignored. Needless to say, uncertainty quantification is crucial for reliable decision-making in medical applications. To fill this gap, we propose a novel Bayesian neural controlled differential equation (BNCDE) for treatment effect estimation in continuous time. In our BNCDE, the time dimension is modeled through …

abstract applications arxiv bayesian continuous cs.lg decision differential however making medical medicine personalized quantification treatment type uncertainty