Title: Reliable Decision Support using Counterfactual Models,
Abstract: Decision-makers are faced with the challenge of estimating what is likely to
happen when they take an action. For instance, if I choose not to treat this
patient, are they likely to die? Practitioners commonly use supervised learning
algorithms to fit predictive models that help decision-makers reason about
likely future outcomes, but we show that this approach is unreliable, and
sometimes even dangerous. The key issue is that supervised learning algorithms
are highly sensitive to the policy used to choose actions in the training data,
which causes the model to capture relationships that do not generalize. We
propose using a different learning objective that predicts counterfactuals
instead of predicting outcomes under an existing action policy as in supervised
learning. To support decision-making in temporal settings, we introduce the
Counterfactual Gaussian Process (CGP) to predict the counterfactual future
progression of continuous-time trajectories under sequences of future actions.
We demonstrate the benefits of the CGP on two important decision-support tasks:
risk prediction and "what if?" reasoning for individualized treatment planning.