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

• URL: http://arxiv.org/abs/2605.05706v1
• Date: Thu, 07 May 2026 05:51:21 GMT
• Title: Resolving the bias-precision paradox with stochastic causal representation learning for personalized medicine
• Abstract summary: Estimating individualized treatment effects from longitudinal observational data is central to data-driven medicine.<n>Here, we identify this tension as a bias-precision paradox in causal representation learning.<n>We introduce sampling-based maximum discrepancy (sMMD) to replace global adversarial with subset-level matching.
• Score: 31.644027724480235
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 introduce sampling-based maximum mean discrepancy (sMMD), a stochastic alignment strategy that replaces global adversarial balancing with subset-level matching. We instantiate this approach in a framework for counterfactual outcome prediction with attribution-grounded interpretability. Across two large-scale ICU cohorts (n = 27,783), our framework improves accuracy under distribution shift, reducing error by up to 11.5% and substantially increasing recall in high-risk tasks. Mechanistic analyses show that sMMD selectively preserves clinically decisive variables. In human-AI evaluation, our method outperforms clinicians-in-training and large language models, and improves clinician accuracy by 14.7% while reducing decision time, enabling interpretable, real-time clinical decision support.

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