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.