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.
In: 2022 IEEE International Conference on Data Mining (ICDM), pp
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Resolving the bias-precision paradox with stochastic causal representation learning for personalized medicine
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.