Causal AI for XRPL/GossipSub network configuration

Many peer-to-peer systems and blockchain platforms rely on underlying communication services, such as GossipSub, which typically operate with default configuration settings. A set of parameters defines these settings, and currently, there is limited understanding of how varying these parameters affects the overall service.
This work proposes a methodology based on Causal AI Discovery to assess the importance of individual parameters on target indicators for the specific case of a popular p2p communication platform. We explore methods to identify factors that influence overall performance and instantiate them for the concrete case of the XRPL blockchain.