Towards Automated Causal Discovery: a case study on 5G telecommunication data
This work addresses the need for automated causal analysis in fields like telecommunications, though it appears incremental as it builds on existing causal methods without claiming major breakthroughs.
The paper tackles the problem of automating causal discovery and reasoning by introducing Automated Causal Discovery (AutoCD), a system designed to deliver causal information akin to an expert analyst, and demonstrates its performance on synthetic data and a case study with 5G telecommunication data.
We introduce the concept of Automated Causal Discovery (AutoCD), defined as any system that aims to fully automate the application of causal discovery and causal reasoning methods. AutoCD's goal is to deliver all causal information that an expert human analyst would and answer a user's causal queries. We describe the architecture of such a platform, and illustrate its performance on synthetic data sets. As a case study, we apply it on temporal telecommunication data. The system is general and can be applied to a plethora of causal discovery problems.