Disease processes are complex, and there is a need to move beyond single-factor epidemiology towards an epidemiological framework that incorporates this complexity. Artificial intelligence methods provide us with an opportunity to rethink traditional epidemiological methods for etiological research. In this project, I aim to develop a novel approach, ‘Sufficient Cause Learning’, which bridges the epidemiological theory of sufficient causes and causal inference with a novel machine learning approach called Layer-wise Relevance Propagation. With this approach, we may come up with new hypotheses of disease etiology. Such hypotheses would need to be rigorously tested using causal models in new cohorts, and hopefully, some of these generated hypothesees of complex interacting causes may lead us to more effective and targeted health interventions. The approach will be developed in collaboration with wold experts in causal inference at UCLA and artificial intelligence in Berlin.
