ETIA: Towards an Automated Causal Discovery Pipeline

Konstantina Biza, Antonios Ntroumpogiannis, Sofia Triantafillou, Ioannis Tsamardinos

Abstract: 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 provide and answer user’s causal queries. To this goal, we introduce ETIA, a system that performs dimensionality reduction, causal structure learning, and causal reasoning. We present the architecture of ETIA, benchmark its performance on synthetic data sets, and present a use case example. The system is general and can be applied to a plethora of causal discovery problems.