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. 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, and causal
inference, can help build explainable, reasoning-aware, and sustainable
wireless networks. Towards fulfilling this vision, we first highlight several
wireless networking challenges that can be addressed by causal discovery and
representation, including ultra-reliable beamforming for terahertz (THz)
systems, near-accurate physical twin modeling for digital twins, training data
augmentation, and semantic communication. 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