AACE is an annotation-assisted method for causal policy learning from multimodal EHRs that outperforms risk-based and representation-based baselines on synthetic, semi-synthetic, and real datasets.
Optimizing multi-scale representations to detect effect heterogeneity using earth observation and computer vision: Application to two anti-poverty rcts
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Annotation-Assisted Learning of Treatment Policies From Multimodal Electronic Health Records
AACE is an annotation-assisted method for causal policy learning from multimodal EHRs that outperforms risk-based and representation-based baselines on synthetic, semi-synthetic, and real datasets.