Unifying Causal Reinforcement Learning: Survey, Taxonomy, Algorithms And Applications

Abstract

Integrating causal inference (CI) with reinforcement learning (RL) has emerged as a powerful paradigm to address critical limitations in classical RL, including low explainability, lack of robustness and generalization failures. Traditional RL techniques, which typically rely on correlation-driven decision-making, struggle when faced with distribution shifts, confounding variables, and dynamic environments. Causal reinforcement learning (CRL), leveraging the foundational principles of causal inference, offers promising solutions to these challenges by explicitly modeling cause-and-effect relationships. In this survey, we systematically review recent advancements at the intersection of causal inference and RL. We categorize existing approaches into causal representation learning, counterfactual policy optimization, offline causal RL, causal transfer learning, and causal explainability. Through this structured analysis, we identify prevailing challenges, highlight empirical successes in

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keycunha2025unifying

Related papers

• Causal Reinforcement Learning: A Survey (2023)0.00
• A Roadmap Towards Improving Multi-agent Reinforcement Learning With Causal Discovery And Inference (2025)0.00
• Learning By Doing: An Online Causal Reinforcement Learning Framework With Causal-aware Policy (2024)1.56
• Learning Nonlinear Causal Reductions To Explain Reinforcement Learning Policies (2025)0.00
• A Comprehensive Survey Of Reinforcement Learning: From Algorithms To Practical Challenges (2024)0.00
• A Survey Of Continual Reinforcement Learning (2025)0.00
• Why Online Reinforcement Learning Is Causal (2024)0.00
• A Survey On Explainable Reinforcement Learning: Concepts, Algorithms, Challenges (2022)0.00