The causal structure learning approach for automatically identifying the configuration parameters with the strongest impact on the optimization objective may face several limitations:
Complexity of Causal Inference: Causal structure learning often requires a large amount of data and computational resources to accurately infer causal relationships. In wireless network optimization, where data collection can be challenging and noisy, the accuracy of causal inference may be compromised.
Confounding Variables: Identifying causal relationships between configuration parameters and optimization objectives can be complicated by the presence of confounding variables. These variables can introduce bias and lead to incorrect causal attributions.
Domain Knowledge Dependency: The effectiveness of causal structure learning relies heavily on the availability of domain knowledge to guide the inference process. In scenarios where domain expertise is limited or incomplete, the accuracy of causal inference may be compromised.
Scalability Issues: Scaling causal structure learning to large-scale wireless networks with numerous configuration parameters and complex interactions can pose challenges in terms of computational complexity and model interpretability.