Foundations of Causal ML
摘要
The present chapter covers the important dimension of causality in ML both in terms of causal structure discovery and causal inference. The vast majority of biomedical ML focuses on predictive modeling and does not address causal methods, their requirements and properties. Yet these are essential for determining and assisting patient-level or healthcare-level interventions toward improving a set of outcomes of interest. Moreover causal ML techniques can be instrumental for health science discovery.