Doubly Robust Functional Representation Learning for Longitudinal Causal Inference with Irregular Histories
不规则历史下纵向因果推断的双重稳健函数型表示学习
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…