CDSP uses an effect-size asymmetry assumption and statistical power to estimate causal directions from bivariate data with uncertainty, reducing false discoveries by 18% on 100 benchmark pairs.
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Develops greedy optimization algorithms for directly learning optimal integer-weighted clinical risk scores, applied to predict post-discharge mortality in a large EHR cohort with a supporting simulation study.
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Causal Discovery via Statistical Power (CDSP)
CDSP uses an effect-size asymmetry assumption and statistical power to estimate causal directions from bivariate data with uncertainty, reducing false discoveries by 18% on 100 benchmark pairs.
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Learning Interpretable Point-Based Clinical Risk Scores via Direct Optimization
Develops greedy optimization algorithms for directly learning optimal integer-weighted clinical risk scores, applied to predict post-discharge mortality in a large EHR cohort with a supporting simulation study.