Resolving the bias-precision paradox with stochastic causal representation learning for personalized medicine

ArXi:2605.05706v1 Announce Type: new Estimating individualized treatment effects from longitudinal observational data is central to data-driven medicine, yet existing methods face a fundamental limitation: reducing confounding bias often suppresses clinically informative heterogeneity, degrading patient-specific predictions. Here, we identify this tension as a bias-precision paradox in causal representation learning and