Modern machine learning techniques are able to learn highly complex associations from data, which has led to amazing progress in computer vision, NLP, and other predictive tasks. However, there are limitations to inference from purely probabilistic or associational information. Without understanding causal relationships, ML models are unable to provide actionable recommendations, perform poorly in new, but related environments, and suffer from a lack of interpretability.
In this talk, I provide an introduction to the field of causal inference, discuss its importance in addressing some of the current limitations in machine learning, and provide some real-world examples from my experience as a data scientist at Brex.
leads the credit risk modeling team at Brex, which researches, develops, deploys, and maintains the automated underwriting models that power Brex. Previously, he was a member of the research staff at IBM AI Research. He holds a Ph.D. in computer science at UCLA, where he was advised by Turing laureate Judea Pearl, and is the author of over 20 peer-reviewed research papers on causal inference, machine learning, economics, and computational finance.