Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations
This addresses the challenge of personalized healthcare decision-making by enabling 'what-if' questions in clinical settings with irregular data, though it is incremental as it builds on existing causal inference and differential equation methods.
The paper tackled the problem of estimating counterfactual outcomes over time with irregularly sampled data by proposing TE-CDE, a method based on neural controlled differential equations, which consistently outperformed existing approaches in simulated tumor growth scenarios.
Estimating counterfactual outcomes over time has the potential to unlock personalized healthcare by assisting decision-makers to answer ''what-iF'' questions. Existing causal inference approaches typically consider regular, discrete-time intervals between observations and treatment decisions and hence are unable to naturally model irregularly sampled data, which is the common setting in practice. To handle arbitrary observation patterns, we interpret the data as samples from an underlying continuous-time process and propose to model its latent trajectory explicitly using the mathematics of controlled differential equations. This leads to a new approach, the Treatment Effect Neural Controlled Differential Equation (TE-CDE), that allows the potential outcomes to be evaluated at any time point. In addition, adversarial training is used to adjust for time-dependent confounding which is critical in longitudinal settings and is an added challenge not encountered in conventional time-series. To assess solutions to this problem, we propose a controllable simulation environment based on a model of tumor growth for a range of scenarios with irregular sampling reflective of a variety of clinical scenarios. TE-CDE consistently outperforms existing approaches in all simulated scenarios with irregular sampling.