This article discusses causal inference, an emerging field in machine learning that goes beyond predicting what could happen to focus on understanding the cause-and-effect relationships in data. The author explains how to detect and fix errors in a directed acyclic graph (DAG) to make it a valid representation of the underlying data.
With all the hype around AI/ML in observability, it's more likely than ever that companies benefit from storing and viewing data in one system and training ML models in another.
