Towards Automated Causal Discovery: a case study on 5G telecommunication data

Causal Discovery is a field of machine learning and statistics aiming to induce causal knowledge from data [29, 47]. There is a large corpus of algorithms and methodologies in the field, spanning tasks like learning causal models, estimating causal effects, and determining optimal interventions. While there are several public libraries of algorithms for these tasks, combining the algorithms and applying them to any given problem is a challenging endeavor that requires extensive knowledge of the methods and a deep understanding of the theory to interpret results. In this paper, we introduce the concept of Automated Causal Discovery (AutoCD) (not to be confused with Automated Causal Inference [14, 26]; see Section 3), defined as the effort to fully automate the application of causal discovery and causal reasoning. AutoCD's goals should be to deliver not just the optimal causal model that fits the data, but all information, answers to queries, visualizations, interpretations, and explanations that a human expert analyst would.