Accounting For Informative Sampling When Learning to Forecast Treatment
  Outcomes Over Time
Toon Vanderschueren Alicia Curth Wouter Verbeke Mihaela van der Schaar
Machine learning (ML) holds great potential for accurately forecasting
treatment outcomes over time, which could ultimately enable the adoption of
more individualized treatment strategies in many practical applications.
However, a significant challenge that has been largely overlooked by the ML
literature on this topic is the presence of informative sampling in
observational data. When instances are observed irregularly over time, sampling
times are typically not random, but rather informative -- depending on the
instance's characteristics, past outcomes, and administered treatments. In this
work, we formalize informative sampling as a covariate shift problem and show
that it can prohibit accurate estimation of treatment outcomes if not properly
accounted for. To overcome this challenge, we present a general framework for
learning treatment outcomes in the presence of informative sampling using
inverse intensity-weighting, and propose a novel method, TESAR-CDE, that
instantiates this framework using Neural CDEs. Using a simulation environment
based on a clinical use case, we demonstrate the effectiveness of our approach
in learning under informative sampling.