Predicting the impact of treatments over time with uncertainty aware neural differential equations
This addresses the problem of counterfactual prediction in healthcare or policy for researchers, offering a method to assess reliable outcomes, though it appears incremental as it builds on existing neural ODE and causal inference techniques.
The paper tackled the challenge of predicting treatment impacts from observational data with confounding, proposing CF-ODE to provide accurate predictions and reliable uncertainty estimates over time, demonstrating improved performance on longitudinal datasets.
Predicting the impact of treatments from observational data only still represents a majorchallenge despite recent significant advances in time series modeling. Treatment assignments are usually correlated with the predictors of the response, resulting in a lack of data support for counterfactual predictions and therefore in poor quality estimates. Developments in causal inference have lead to methods addressing this confounding by requiring a minimum level of overlap. However,overlap is difficult to assess and usually notsatisfied in practice. In this work, we propose Counterfactual ODE (CF-ODE), a novel method to predict the impact of treatments continuously over time using Neural Ordinary Differential Equations equipped with uncertainty estimates. This allows to specifically assess which treatment outcomes can be reliably predicted. We demonstrate over several longitudinal data sets that CF-ODE provides more accurate predictions and more reliable uncertainty estimates than previously available methods.