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, trea...
| Authors | Mengfei Ran, Yifeng Shen, Ruijie Guan |
|---|---|
| Primary category | stat.ML |
| Published | 30 July 2026 |
| Last updated at source | 30 July 2026 |
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