“Predicting the impact of treatments over time with uncertainty aware neural differential equations”
Speaker: Edward De Brouwer (KU Leuven)
Predicting the impact of interventions in the real world from observational data alone represents a major statistical challenge. Indeed, 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 not satisfied in practice. In this work, we propose to circumvent the overlap assumption by predicting the impact of treatments continuously over time using neural ordinary differential equations equipped with uncertainty estimates.
This presentation was held virtually on January 19, 2023 @ 11:00 AM