This repository hosts the materials for a seminar paper developed as part of the Novel and non-mainstream advances in Data Science course at KIT. The paper focuses on causal discovery from log data, with a special emphasis on time-series data. It provides a comprehensive review of computational methods for causal discovery, including constraint-based, score-based methods, and those based on functional causal models.
For a detailed presentation of the seminar, please visit the following link: Seminar Presentation.