Fifth Conference on Causal Learning and Reasoning

The Fifth Conference on Causal Learning and Reasoning (CLeaR 2026) is dedicated to one of the most important open problems in AI and statistics: moving from correlation to causation. As machine learning systems are increasingly deployed in high-stakes domains, the inability to reason causally—to understand what would happen under interventions, counterfactuals, or policy changes—becomes a critical scientific and practical limitation.

Researchers in causal inference, machine learning, statistics, philosophy, and the social and health sciences gather at CLeaR to present advances in causal discovery algorithms, identifiability theory, counterfactual reasoning, causal representation learning, and the application of causal methods to fairness, healthcare, and policy evaluation. CLeaR 2026 is the premier dedicated venue for a growing community that believes causal reasoning is not a niche statistical specialty but a foundational requirement for trustworthy AI.