Stabilized Neural Prediction of Potential Outcomes in Continuous Time

TL;DR

This paper introduces SCIP-Net, a novel neural network that estimates potential treatment outcomes in continuous time, addressing the limitation of existing methods that only handle fixed time steps in medical data.

Contribution

We develop SCIP-Net, the first neural approach capable of adjusting for time-varying confounding in continuous-time patient trajectory modeling.

Findings

First neural method for continuous-time confounding adjustment

Robust estimation of potential outcomes in irregular time series

Improved personalization of medical treatment predictions

Abstract

Patient trajectories from electronic health records are widely used to estimate conditional average potential outcomes (CAPOs) of treatments over time, which then allows to personalize care. Yet, existing neural methods for this purpose have a key limitation: while some adjust for time-varying confounding, these methods assume that the time series are recorded in discrete time. In other words, they are constrained to settings where measurements and treatments are conducted at fixed time steps, even though this is unrealistic in medical practice. In this work, we aim to estimate CAPOs in continuous time. The latter is of direct practical relevance because it allows for modeling patient trajectories where measurements and treatments take place at arbitrary, irregular timestamps. We thus propose a new method called stabilized continuous time inverse propensity network (SCIP-Net). For this, we further derive stabilized inverse propensity weights for robust estimation of the CAPOs. To the best of our knowledge, our SCIP-Net is the first neural method that performs proper adjustments for time-varying confounding in continuous time.