WHAT/IF: Leveraging Causal Machine Learning at Netflix
Most Machine Learning algorithms used in Personalization and Search, including Deep Learning, are purely associative, and learn from the correlations between features and outcomes. In many scenarios, going beyond the purely associative nature, and understanding the causal mechanism between taking a certain action and the resulting outcome becomes the key in decision making. Causal Inference gives us a principled way of learning such relationships, and when married with machine learning, becomes a powerful tool that can be leveraged at scale. In this talk, we will give a high level overview of how Netflix is using Causal Machine Learning in various applications. We will go over different flavors of Causal ML techniques we are exploring at Netflix, the learnings, the challenges, and discuss future directions.
Aish is a Director of Machine Learning at Netflix. His org is responsible for the core recommendation and search algorithms used at Netflix. Aish has over 23 years of experience at the intersection of mathematics and software engineering. Prior to Netflix, Aish lead the data science teams at Opentable, Foodspotting, iVistra, and founded the company, vWork, solving large-scale optimization problems.