Causal Effect Inference: A Machine Learning Approach
A major challenge in the domain of healthcare is ascertaining whether a given treatment influences or determines an outcome—for instance, whether there is a survival benefit to prescribing a certain medication. Current treatment guidelines have been developed with the “average” patient in mind, but the reality is that treatments result in different effects and outcomes from one individual to another.
Using AI and machine learning, we can endeavor to understand the effect of specific treatments on specific patients at specific times, given their unique characteristics. This is what we call causal effect inference, or individualized treatment effect inference. This is far from a straightforward undertaking, however. In this seminar, I will offer an introduction to individualized treatment effect inference for healthcare. I will explain the importance of this research area, while also highlighting some key challenges, formalisms, methodologies, and applications.
This event will take place online on May 3 at 13:00 PDT (21:00 BST).
The mission of the The Halıcıoğlu Data Science Institute (HDSI) is to lay the groundwork for the scientific foundations of this emerging discipline, develop new methods and infrastructure, and train students, faculty and industry partners to use data science in ways that will allow them to solve some of the world’s most pressing problems. We serve as a unique, collaborative and innovative academic unit across multiple disciplines at UC San Diego.