This post is by Eric.

We are resuming our Webinar series this Thursday with Uri Shalit from Technion. You can register here.

Abstract

A plethora of new methods for estimating patient-level causal effects have been proposed recently, focusing on what is technically known as (high-dimensional) conditional average effects (CATE). The intended use of many of these methods is to inform human decision-makers about the probable outcomes of possible actions, for example, clinicians choosing among different medications for a patient. For such high-stakes decisions, it is crucial for any algorithm to responsibly convey a measure of uncertainty about its output, in order to enable informed decision making on the side of the human and to avoid catastrophic errors.

We will discuss recent work where we present new methods for conveying uncertainty in CATE estimation stemming from several distinct sources: (i) finite data (ii) covariate shift (iii) violations of the overlap assumption (iv) violation of the no-hidden confounders assumption. We show how these measures of uncertainty can be used to responsibly decide when to defer decisions to experts and avoid unwarranted errors.

This is joint work with Andrew Jesson, Sören Mindermann, and Yarin Gal of Oxford University.

About the speaker

Uri Shalit is an Assistant Professor in the Faculty of Industrial Engineering and Management at Technion University. He received his Ph.D. in Machine Learning and Neural Computation from the Hebrew University in 2015.  Prior to joining Technion, Uri was a postdoctoral researcher at NYU working with prof. David Sontag.

Uri’s research is currently focused on three subjects. The first is applying machine learning to the field of healthcare, especially in terms of providing physicians with decision support tools based on big health data. The second subject Uri is interested in is the intersection of machine learning and causal inference, especially the problem of learning individual-level effects. Finally, Uri is working on bringing ideas from causal inference into the field of machine learning, focusing on problems in robust learning, transfer learning, and interpretability.

The video is available here.