Precision Medicine and Causal Inference

In practice, clinicians synthesize information on a patient’s current and past health status, medical history, genomic profile, environment and lifestyle, and other factors to make tailored treatment decisions over the course of his or her disease or disorder. Precision medicine involves making this clinical decision-making process evidence based — that is, based on evidence from data. This requires methodology for translating existing data and data collected for this purpose into evidence-based, optimal strategies for what is often sequential decision making. These methods must incorporate evaluation of the causal effects of treatments and interventions.

The research these faculty carry out merges techniques from statistics and causal inference with those from fields such as machine learning and artificial intelligence to develop new methods for data-driven decision making.

Faculty

Marie Davidian
Eric Laber
Wenbin Lu
Rui Song
Shu Yang