Causality in Machine Learning: A 2020 Review

The article likely discusses the advancements and challenges in incorporating causal reasoning into machine learning models, specifically focusing on the state of the field in 2020. Analyzing causality is crucial for creating more robust and explainable AI systems, moving beyond simple correlation to understanding cause-and-effect relationships.

• •Causality allows models to move beyond correlation to understanding causal relationships.
• •2020 was a significant year in the research of causality for ML.
• •Understanding causality is critical for developing more robust and interpretable AI.