Causal Models in Machine Learning

This is the video archive of the February 1, 2020 TWIML webinar Causal Modeling in Machine Learning. In the webinar, Robert Ness, machine learning research engineer and instructor, introduces the core concepts underlying causality and causal models in machine learning. Building on ideas like association, intervention and counterfactuals, Robert provides an overview of how to build a causal reasoning engine, and the role of DAGs (directed, acyclic graphs) and probabilistic programming in causal modeling. Finally he explores the intersection of causal modeling and deep learning.

The remainder of the session is dedicated to a Q&A on the Causality courses and study group Robert is offering in partnership with TWIML. Robert has developed a series of six course modules on Causal Modeling in Machine Learning. He is teaching the course live to graduate students at Northeastern University, and via recorded videos through his web site. Through the TWIML partnership, Robert will be hosting a study group via the TWIML Community platform. This is will give participants in the TWIML group some of the benefits of taking the course live. Robert will hold a review session after each week of study in the sequence, will be available to answer questions via Slack, will personally grade submitted assignments, and will be available to assist with course homework and projects.

For more information on the courses, visit

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