Beyond Correlations: The Necessity and the Challenges of Causal AI

Over the past decade, artificial intelligence (AI) has achieved remarkable breakthroughs in diverse fields, with much of this progress stemming from correlational AI, which harnesses vast datasets and computing to detect patterns and make predictions. Despite these achievements, correlational AI often fails when confronted with distribution shifts, struggles to make predictions under interventions, yields superficial explanations, and can perpetuate biases. These shortcomings highlight the fundamental gap between pattern recognition and causal understanding, limiting the development of robust and responsible AI systems. To address these shortcomings, the emerging field of Causal AI synergises the predictive power of modern AI with causal reasoning, explicitly modelling cause-and-effect relationships through interventions and counterfactuals within formal frameworks like Structural Causal Models or Potential Outcomes. We present a conceptual map of Causal AI, specifically designed for AI researchers new to the field, by uniquely structuring this introductory review around four key questions: (1) What are causality and causal AI: an introduction to core concepts and frameworks. (2) Why is causality needed: a discussion of the fundamental limitations of correlational AI. (3) What are the foundational challenges inherent in causal reasoning itself. (4) What are the key emerging research directions at the intersection of causality and AI. Ultimately, this paper aims to provide the rationale and motivation for the AI community to explore why synergising causality with AI holds substantial promise for developing responsible AI systems.