When analyzing longitudinal medical data with irregular measurements, you can use neural encoders to create summaries that maintain the statistical properties needed for valid causal inference—representation error behaves like ordinary estimation error under explicit conditions.
This paper addresses causal inference from messy medical data with irregular measurements (lab values, vital signs) taken at different times. The authors propose DR-FRL, a method that converts these fragmented histories into meaningful summaries while preserving statistical guarantees.