Ehud Karavani
Ehud is a research staff member at IBM Research, marrying machine learning with causal inference to address questions in medicine and healthcare.
He combines applied research with tool development for research, having created and currently maintaining Causallib—an open-source Python package for flexible causal inference modeling—used by many practitioners in both academia and industry. Over his 8 years at IBM, he has led the causality strategy for the company's global efforts in drug discovery, consulted to many of its research labs worldwide, lectured on causality to staff and clients, developed novel methodologies and published his research.
He holds an MSc. in computer science and computational biology from the Hebrew University, where he worked on trait prediction using DNA and assessed its potential consequences for population genetics and embryo selection. A musician and hiker, but mostly a parent.
Sessions
Imagine the newest medical prediction algorithm is claiming you have high-risk for some health condition. I bet the first thing going through your mind is "well, what can I do to reduce it".
Regular prediction is not always enough, we often care about predicting the consequences of several paths of action we can take - the causal effect of these actions.
In this talk I will briefly present causal inference - the science of estimating causal effect of actions using observational data and how it differs from regular prediction. I will overview models for estimating causal effect and how to apply them with causallib - a one-stop-shop open-source Python package for flexible causal inference modeling.