Stochastic causal representation learning with sMMD resolves the bias-precision paradox, improving accuracy under distribution shift by up to 11.5% and clinician accuracy by 14.7% on large ICU cohorts.
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Robust optimization framework for green ammonia that ensures feasible capacity plans under renewable uncertainty where constraint aggregation fails, using scenario reduction and adaptive policies.
Researchers derived eight design requirements, seven principles, and nine features for causal machine learning-based clinical decision support systems from literature and physician interviews.
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