Doubly Robust Functional Representation Learning for Longitudinal Causal Inference with Irregular Histories
Making sense of messy medical records to find what actually causes outcomes
Doctors often track patients with lab tests and monitor readings taken at random times, creating fragmented records that standard statistical methods struggle to handle. This paper presents a new approach that converts these irregular histories into clean data states, then uses those states to answer causal questions—like whether a treatment actually changed a patient's outcome—while accounting for confounding and measurement bias. Tests on ICU data show the method works even when records are sparse or heavily skewed.
Hospital records are inherently messy: labs are ordered when doctors suspect problems, vital signs stream continuously but get recorded sporadically, and timing itself contains medical information. Current methods either throw away the timing and detail or ignore bias in what gets measured when. This approach handles all of that without requiring researchers to manually engineer summaries, making it possible to reliably answer causal questions from real electronic health records—which matters because many treatment decisions in medicine depend on understanding what actually works, not just what correlates with recovery.