Performing Root Cause Analysis with DoWhy, a Causal Machine-Learning Library
In this talk, we will introduce the audience to DoWhy, a library for causal machine-learning (ML). We will introduce typical problems where causal ML can be applied and will specifically do a deep dive on root cause analysis using DoWhy. To do this, we will lay out what typical problem spaces for causal ML look like, what kind of problems we're trying to solve, and then show how to use DoWhy's API to solve these problems. Expect to see a lot of code and very hands-on code examples. We will close this session by zooming out a bit and also talk about the PyWhy organization governing DoWhy.
Patrick Blöbaum is a Senior Applied Scientist at AWS, where he develops, implements and applies novel causal inference methods to business problems. He is also a main contributor to the open-source library DoWhy and the PyWhy organization. Prior to working at AWS, he got his PhD degree in the area of causality focusing on graphical causal models. His research interests include topics such as root cause analysis and causal discovery.