# Causal Reasoning: Charting a Revolutionary Course for Next-Generation AI-Native Wireless Networks

arXiv: https://arxiv.org/abs/2309.13223 · HTML: https://mlsanity.com/paper/2309.13223/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning.
- Builds on 24 of its 34 references that are in the map.
- First posted on arXiv 23 Sep 2023.

## Placed in

- LLM reasoning / [Reasoning for agents, planning and code](https://mlsanity.com/lr/reasoning-for-agents-planning-and-code/)

## Builds on (most cited first, 24 of 24)

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [In-context Reinforcement Learning with Algorithm Distillation](https://mlsanity.com/paper/2210.14215/) (2022)
- [Explainable Reinforcement Learning Through a Causal Lens](https://mlsanity.com/paper/1905.10958/) (2019)
- [Bayesian Structure Learning with Generative Flow Networks](https://mlsanity.com/paper/2202.13903/) (2022)
- [Deep Learning in Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/1803.04311/) (2018)
- [Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data](https://mlsanity.com/paper/2006.10833/) (2020)
- [Can we trust the evaluation on ChatGPT?](https://mlsanity.com/paper/2303.12767/) (2023)
- [Less Data, More Knowledge: Building Next Generation Semantic Communication Networks](https://mlsanity.com/paper/2211.14343/) (2022)
- [Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial](https://mlsanity.com/paper/2011.03615/) (2020)
- [Channel Charting: Locating Users within the Radio Environment using Channel State Information](https://mlsanity.com/paper/1807.05247/) (2018)
- [Causal Bayesian Optimization](https://mlsanity.com/paper/2005.11741/) (2020)
- [Understanding Telecom Language Through Large Language Models](https://mlsanity.com/paper/2306.07933/) (2023)
- [Experienced Deep Reinforcement Learning with Generative Adversarial Networks (GANs) for Model-Free Ultra Reliable Low Latency Communication](https://mlsanity.com/paper/1911.03264/) (2019)
- [Neuro-Symbolic Causal Reasoning Meets Signaling Game for Emergent Semantic Communications](https://mlsanity.com/paper/2210.12040/) (2022)
- [Reasoning about Causality in Games](https://mlsanity.com/paper/2301.02324/) (2023)
- [A Bayesian Framework for Digital Twin-Based Control, Monitoring, and Data Collection in Wireless Systems](https://mlsanity.com/paper/2212.01351/) (2022)
- [Learning Resilient Radio Resource Management Policies with Graph Neural Networks](https://mlsanity.com/paper/2203.11012/) (2022)
- [Causal Semantic Communication for Digital Twins: A Generalizable Imitation Learning Approach](https://mlsanity.com/paper/2304.12502/) (2023)
- [Joint Sensing, Communication, and AI: A Trifecta for Resilient THz User Experiences](https://mlsanity.com/paper/2305.00135/) (2023)
- [Emergence of Theory of Mind Collaboration in Multiagent Systems](https://mlsanity.com/paper/2110.00121/) (2021)
- [Artificial Intelligence in 3GPP 5G-Advanced: A Survey](https://mlsanity.com/paper/2305.05092/) (2023)
- [Rethinking Data-driven Networking with Foundation Models: Challenges and Opportunities](https://mlsanity.com/paper/2211.06494/) (2022)
- [On Multi-objective Policy Optimization as a Tool for Reinforcement Learning: Case Studies in Offline RL and Finetuning](https://mlsanity.com/paper/2106.08199/) (2021)
- [Reliable Beamforming at Terahertz Bands: Are Causal Representations the Way Forward?](https://mlsanity.com/paper/2303.08017/) (2023)

## Built on by (most cited first, 16 of 16)

- [AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges](https://mlsanity.com/paper/2505.10468/) (2025)
- [Artificial General Intelligence (AGI)-Native Wireless Systems: A Journey Beyond 6G](https://mlsanity.com/paper/2405.02336/) (2024)
- [Large Multi-Modal Models (LMMs) as Universal Foundation Models for AI-Native Wireless Systems](https://mlsanity.com/paper/2402.01748/) (2024)
- [What If We Had Used a Different App? Reliable Counterfactual KPI Analysis in Wireless Systems](https://mlsanity.com/paper/2410.00150/) (2024)
- [Internet of Federated Digital Twins (IoFDT): Connecting Twins Beyond Borders for Society 5.0](https://mlsanity.com/paper/2312.06432/) (2023)
- [Bridging Physical and Digital Worlds: Embodied Large AI for Future Wireless Systems](https://mlsanity.com/paper/2506.24009/) (2025)
- [LLMs' Suitability for Network Security: A Case Study of STRIDE Threat Modeling](https://mlsanity.com/paper/2505.04101/) (2025)
- [Confounding-Valid Conformal Inference for Counterfactual KPIs in Wireless Networks](https://mlsanity.com/paper/2609.05073/) (2026)
- [Causal Discovery in Dynamic Fading Wireless Networks](https://mlsanity.com/paper/2506.03163/) (2025)
- [Distributionally Robust Wireless Semantic Communication with Large AI Models](https://mlsanity.com/paper/2506.03167/) (2025)
- [AGI Enabled Solutions For IoX Layers Bottlenecks In Cyber-Physical-Social-Thinking Space](https://mlsanity.com/paper/2506.22487/) (2025)
- [Causal Beam Selection for Reliable Initial Access in AI-driven Beam Management](https://mlsanity.com/paper/2508.16352/) (2025)
- [Causal Model-Based Reinforcement Learning for Sample-Efficient IoT Channel Access](https://mlsanity.com/paper/2511.10291/) (2025)
- [BRAIN: Bayesian Reasoning via Active Inference for Agentic and Embodied Intelligence in Mobile Networks](https://mlsanity.com/paper/2602.14033/) (2026)
- [From Passive Mirrors to Active Agents: Holonic Digital Twins for Physical AI over Networks](https://mlsanity.com/paper/2608.06227/) (2026)
- [On the Identifiability of Mixed Ordinal and Exponential Family Causal DAGs under Linear Parametric Models](https://mlsanity.com/paper/2609.17942/) (2026)

## Abstract (arXiv)

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.