Learning to Act from Observational Data: Machine Learning and Causal Inference in Healthcare by Uri Shalit (Technion) – DSI Learning Club

June 7th, Thu 10:00 , Uri Shalit (webpage).

Technion – Israel Institute of Technology (Faculty).

Location: Gonda Building (901), Room 101.

Learning to Act from Observational Data: Machine Learning and Causal Inference in Healthcare

Abstract:

The proliferation of data collection in the health, commercial, and economic spheres, brings with it opportunities for extracting new knowledge leading to concrete policy implications. An example that motivates my research is using electronic healthcare records to individualize medical practices.

The scientific challenge lies in the fact that standard prediction models such as supervised machine learning are often not enough for decision making from this so-called “observational data”: Supervised learning does not take into account causality, nor does it account for the feedback loops that arise when predictions are turned into actions. On the other hand, existing causal-inference methods are not adapted to dealing with the rich and complex data now available, and often focus on populations, as opposed to individual-level effects.

In my talk, I will discuss the challenges of applying machine learning in the clinical healthcare setting, and show how we apply recent ideas from machine learning and specifically deep-learning to individual-level causal-inference and action.