Causal ML for Healthcare and Life Sciences

Using Causal Machine Learning for Healthcare and Life Sciences


The Causal Machine-Learning for Healthcare and Life Sciences team includes researchers from the fields of physics, computer science, statistics, and epidemiology. We conduct analyses for many types of healthcare-related data, including electronic health records and insurance claims data holding the medical history of over 150 million people.

More recently, our efforts are focused on causal inference, which answers the question "what is the effect of doing something?" We used this technology to create an efficient engine to identify drug-repurposing candidates and have released the base causal inference technology as open-source code. In general, we develop technology that allows the acceleration of meaningful clinical discoveries based on real-world data.