Invited Talk: Generalizability, robustness and fairness in machine learning risk prediction models

By leveraging principles of health equity, I will discuss the use of causal models and machine learning to address realistic challenges of data collection and model use across environments. Examples include a domain adaptation approach that improves prediction in under-represented population sub-groups by leveraging invariant information across groups when possible, and an algorithmic fairness method which specifically incorporates structural factors to better account for and address sources of bias and disparities.