A pipeline of temporal causal normalizing flows and LLM-based evolutionary imputation estimates treatment effects from incomplete EHRs, recovering a weight-loss difference consistent with randomized evidence in a diabetes study.
Treatment is absorbing: once assigned it persists
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Joint Treatment Effect Estimation from Incomplete Healthcare Data: Temporal Causal Normalizing Flows with LLM-driven Evolutionary MNAR Imputation
A pipeline of temporal causal normalizing flows and LLM-based evolutionary imputation estimates treatment effects from incomplete EHRs, recovering a weight-loss difference consistent with randomized evidence in a diabetes study.