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 practical applications, and discuss open problems. Finally, we provide future research directions, underscoring the potential of CRL for developing robust, generalizable, and interpretable artificial intelligence systems.
Abstract (translated)
将因果推断（CI）与强化学习（RL）相结合，已成为解决经典RL中关键限制的强大范式，包括低可解释性、缺乏鲁棒性和泛化失败。传统的RL技术通常依赖于基于相关性的决策制定，在面对分布变化、混淆变量和动态环境时表现不佳。因果强化学习（CRL），通过利用因果推断的基本原理，通过显式建模因果关系提供了有希望的解决方案。在这篇综述中，我们系统地回顾了因果推断与RL交汇处的最新进展。我们将现有的方法分为因果表示学习、反事实策略优化、离线因果RL、因果迁移学习和因果可解释性等类别。通过对这些领域的结构化分析，我们识别出当前面临的挑战，并突出其实用应用中的实证成功案例，同时讨论开放的问题。最后，我们提供了未来的研究方向，强调CRL在开发稳健、通用且可解释的人工智能系统方面的潜力。
URL
https://arxiv.org/abs/2512.18135
