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Evidence record 4862 · automatically gathered

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

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 ad

Record details

Published: 7 May 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Safety & alignment · Healthcare
Retrieved: 14 July 2026

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These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Shared topic: Bias & fairness · Shared topic: Safety & alignment

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How to cite this record

ethics.ai (7 May 2026), “Resolving the bias-precision paradox with stochastic causal representation learning for personalized medicine,” evidence record 4862, https://ethics.ai/record/4862 (originally published by arXiv).

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