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.
Sudeep is a Machine Learning Area Lead at Netflix, where his main focus is on developing the next generation of machine learning algorithms to drive the personalization, discovery and search experience in the product. Apart from algorithmic work, he also takes a keen interest in data visualizations. Sudeep has had more than fifteen years of experience in machine learning applied to both large scale scientific problems, as well as in the industry. He holds a PhD in Astrophysics from Princeton University.