这篇提出DR-FRL，把不规则化验点云变成因果推断能用的状态，还在VitalDB上验证，结论是标量摘要其实够用。
DR-FRL是一种面向不规则历史的纵向因果推断方法，将功能编码器与时间编码器结合，把点云和既往史映射为估计目标相关的状态。模拟实验显示，在功能混杂高维、测量信息性、支持度弱或伪结果重尾时，DR-FRL比标准序列学习更稳定。在VitalDB审计中，DR-FRL使用不规则实验室点云得到负面发现：对于ICU处置终点，标量实验室摘要已携带大部分相关信息。该方法通过EIF靶向验证、校准、重叠和尾部分析评估状态是否支撑估计方程。
Doubly Robust Functional Representation Learning for Longitudinal Causal Inference with Irregular Histories
Longitudinal causal studies often record histories as irregular functional fragments: laboratory values, physiologic signals, sensor streams, and image-derived summaries measured at unequal and informative times. Standard doubly robust estimators usually require scalar summaries, whereas sequence learners optimize prediction losses that need not stabilize the efficient influence function. We propose Doubly Robust Functional Representation Learning (DR-FRL), a cross-fitted workflow that turns irregular histories into estimand-targeted states for observed-history regimes. Functional and temporal encoders map point clouds and prior histories into states; nuisance heads estimate outcome, treatment, and censoring functions; and EIF-targeted validation, calibration, overlap, tail, and ablation diagnostics assess whether the state supports the estimating equation. If the selected state preserves the nuisance information needed by the EIF, representation error enters the same second-order product remainder as ordinary nuisance error, and the mean estimator is asymptotically linear under explicit rate, overlap, calibration, and stability conditions. Catoni aggregation is treated separately as a bounded-influence point estimator, not a replacement for Wald inference. Simulations show gains when functional confounding is high-dimensional, measurement is informative, support is weak, or pseudo-outcomes are heavy-tailed. A VitalDB audit shows that DR-FRL can use irregular laboratory point clouds and deliver a useful negative finding: for this ICU-disposition endpoint, scalar laboratory summaries already carry much endpoint-relevant information.