Web servers usually use predefined configurations, yet empirical studies have shown that performance can be significantly improved when the configurations of the networking protocol stack (e.g., TCP, QUIC, and congestion control parameters) are carefully tuned due to the fact that a “one-size-fits-all” strategy does not exist. However, dynamically tuning the protocol stack's configurations is challenging: first, the configuration space is ample, and parameters with complex dependencies must be tuned jointly; second, the network condition space is also large, so an adaptive solution is needed to handle clients' diversity and network dynamics; and finally, clients endure unsatisfactory performance degradation due to learning exploration. To this end, we propose Config-Snob, a protocol tuning solution that selects the best configurations based on historical data. Config-Snob exploits the configuration space by tuning several configuration knobs and provides a practical fine-grained client grouping while handling the network environment dynamics. Config-Snob uses a controlled exploration approach to minimize the performance degradation. Config-Snob utilizes causal inference (CI) algorithms to boost the tuning optimization. Config-Snob is implemented in a QUIC-based server and deployed in a large-scale production environment. Our extensive experiments show that the proposed solution improves the completion time over the default configurations by 15% to 36% (mean) and 62% to 70% (median) in the real deployment.