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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Online Access: | https://arxiv.org/abs/2605.05706 |
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| _version_ | 1866911654967508992 |
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| author | Zhang, Peisong Peng, Manqiang Wu, Yuxuan Phadungsaksawasdi, Pawit Yeung, Wesley Zhang, Ye Nguyen, Trang Zhang, Qiang Liu, Nan Wang, Meng Ngiam, Kee Yuan Tham, Yih-Chung Cheng, Ching-Yu Fu, Tianfan Chen, Qingyu Ke, Rosemary Li, Chang Yang, Wenzhuo Lu, Zhenghao Lai, Chunyou Zhang, Yu Zhong, Sheng Deng, Hao Liu, Dianbo |
| author_facet | Zhang, Peisong Peng, Manqiang Wu, Yuxuan Phadungsaksawasdi, Pawit Yeung, Wesley Zhang, Ye Nguyen, Trang Zhang, Qiang Liu, Nan Wang, Meng Ngiam, Kee Yuan Tham, Yih-Chung Cheng, Ching-Yu Fu, Tianfan Chen, Qingyu Ke, Rosemary Li, Chang Yang, Wenzhuo Lu, Zhenghao Lai, Chunyou Zhang, Yu Zhong, Sheng Deng, Hao Liu, Dianbo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_05706 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Resolving the bias-precision paradox with stochastic causal representation learning for personalized medicine Zhang, Peisong Peng, Manqiang Wu, Yuxuan Phadungsaksawasdi, Pawit Yeung, Wesley Zhang, Ye Nguyen, Trang Zhang, Qiang Liu, Nan Wang, Meng Ngiam, Kee Yuan Tham, Yih-Chung Cheng, Ching-Yu Fu, Tianfan Chen, Qingyu Ke, Rosemary Li, Chang Yang, Wenzhuo Lu, Zhenghao Lai, Chunyou Zhang, Yu Zhong, Sheng Deng, Hao Liu, Dianbo Artificial Intelligence Quantitative Methods 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. |
| title | Resolving the bias-precision paradox with stochastic causal representation learning for personalized medicine |
| topic | Artificial Intelligence Quantitative Methods |
| url | https://arxiv.org/abs/2605.05706 |