Despite the basic premise that next-generation wireless networks (e.g., 6G) will be artificial intelligence (AI)-native, to date, most existing efforts remain either qualitative or incremental extensions to existing “AI for wireless” paradigms. Indeed, creating AI-native wireless networks faces significant technical challenges due to the limitations of data-driven, training-intensive AI. These limitations include the black-box nature of the AI models; their curve-fitting nature, which can limit their ability to reason and adapt; their reliance on large amounts of training data; and the energy inefficiency of large neural networks (NNs). In response to these limitations, this article presents a comprehensive, forward-looking vision that addresses these shortcomings by introducing a novel framework for building AI-native wireless networks, grounded in the emerging field of causal reasoning. Causal reasoning, founded on causal discovery, causal representation learning (CRL), and causal inference, can help build explainable, reasoning-aware, and sustainable wireless networks. Embracing such a human-like AI foundation can revolutionize the design of AI-native wireless networks, laying the foundations for creating self-sustaining networks that ensure uninterrupted connectivity. Toward fulfilling this vision, we first highlight several wireless networking challenges that can be addressed by causal discovery and representation, including ultrareliable beamforming for terahertz (THz) systems, near-accurate physical twin modeling for digital twins (DTs), training data augmentation, and semantic communication (SC). We showcase how incorporating causal discovery can assist in achieving dynamic adaptability, resilience, and cognition in addressing these challenges. Furthermore, we outline potential frameworks that leverage causal inference to achieve the overarching objectives of future-generation networks, including intent management, dynamic adaptability, human-level cognition, reasoning, and the critical element of time sensitivity. We conclude by offering recommendations shaping the roadmap toward causality-driven AI-native next-generation wireless networking.

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因果推理：为下一代人工智能原生无线网络制定革命性路线

尽管基本前提是下一代无线网络（例如 6G）将是人工智能（AI）原生的，但迄今为止，大多数现有工作仍然是对现有“无线人工智能”范式的定性或增量扩展。事实上，由于数据驱动、训练密集型人工智能的局限性，创建人工智能原生无线网络面临着重大的技术挑战。这些限制包括人工智能模型的黑盒性质；他们的曲线拟合性质，这会限制他们的推理和适应能力；他们对大量训练数据的依赖；以及大型神经网络（NN）的能源效率低下。针对这些限制，本文提出了一个全面的、前瞻性的愿景，通过引入一种基于新兴因果推理领域的构建人工智能原生无线网络的新颖框架来解决这些缺点。因果推理建立在因果发现、因果表示学习 (CRL) 和因果推理的基础上，可以帮助构建可解释、推理感知和可持续的无线网络。采用这种类人的人工智能基础可以彻底改变人工智能原生无线网络的设计，为创建确保不间断连接的自我维持网络奠定基础。为了实现这一愿景，我们首先强调可以通过因果发现和表示来解决的几个无线网络挑战，包括太赫兹（THz）系统的超可靠波束成形、数字孪生（DT）的近精确物理孪生建模、训练数据增强以及语义沟通（SC）。我们展示了如何结合因果发现来帮助实现动态适应性、弹性和认知来应对这些挑战。此外，我们概述了利用因果推理来实现下一代网络的总体目标的潜在框架，包括意图管理、动态适应性、人类认知、推理和时间敏感性的关键要素。最后，我们提供了制定因果驱动的人工智能原生下一代无线网络路线图的建议。