Machine Learning-based Predictive Inference of Individualized Treatment Effects

Seminar | March 22 | 4-5 p.m. | 1011 Evans Hall

Speaker/Performer:  Ahmed Alaa, UC Berkeley and UCSF

Sponsor:  Department of Statistics

Machine learning (ML) methods, combined with large-scale electronic health databases, could enable a personalized approach to healthcare by improving patient-specific diagnosis, prognostic predictions, and treatment decisions. In this talk, I will focus on the problem of predictive inference on the effect of a treatment on individual patients using machine learning models applied to observational data. First, I will describe some Bayesian approaches to tackle this problem using Gaussian processes. Next, I will discuss model-free approaches for predictive inference based on conformal prediction that provide frequentist coverage guarantees. In particular, I will describe new methods for inference on ITEs by applying conformal prediction on top of pseudo-outcome regression models in a post-hoc fashion. Finally, I will discuss exciting avenues for future work.

Event Contact:  zhivotovskiy@berkeley.edu, 5102292370

Access Coordinator:  Nikita Zhivotovskiy,  zhivotovskiy@berkeley.edu,  510-229-2370