SMILES 2020: Causality and Increasing Model Reliability

When building healthcare AI / predictive tools in the real world, certain issues should be considered and accounted for, such as understanding how the tool generalizes from one site to another, how it stands up to changes in physician practice patterns, and how sensitive it is to feedback loops.

In this tutorial, Suchi Saria discusses state of the art research on combining AI and causal inference to build models that are safe and robust in use and practice.

See the full tutorial here.