04-17, 15:45–16:30 (Europe/Berlin), A03-A04
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 with a hands-on example. We will close this session by zooming out a bit and also talk about the PyWhy organization governing DoWhy.
"Much like machine learning libraries have done for prediction, DoWhy is a Python library that aims to spark causal thinking and analysis. DoWhy provides a wide variety of algorithms for effect estimation, causal structure learning, diagnosis of causal structures, root cause analysis, interventions and counterfactuals."
The field of causal machine-learning (ML) is not as well-known as typical machine-learning problems and libraries. DoWhy is one of the more popular open-source libraries for causal ML. And not for nothing: DoWhy is based on the two major scientific frameworks, Potential Outcome and Graphical Causal Models and offers a large variety of features.
Problems where causal ML can be applied, come from any imaginable domain, be that distributed computer systems, supply chain, workflow management, manufacturing, etc. As long as a complex system can be represented as a causal graph, one can also apply causal ML.
In the talk, we will specifically dive into a microservice architecture, as this is an example which an audience like the one at PyCon can most likely relate to. We will present some data and then inject outliers (or anomalies) into that data, see how those propagate through the system, and then use DoWhy's algorithms to show us the root cause.
By the end of the talk, the audience should have a good understanding of typical problem domains for causal ML and a good sense of how to use DoWhy to solve such problems.
Intermediate
Public link to supporting material – Expected audience expertise: Python –Novice
Abstract as a tweet –Learn how to use the Python DoWhy library to perform root cause analysis using methods of causal machine-learning.
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.