Causal Inference Failures in Machine-Learning-Driven Database Tuning

Abstract

Machine-learning-driven database performance tuning has shown strong potential in optimizing complex systems, but its reliance on observational data often leads to causal inference failures that compromise reliability. Existing approaches focus on predictive accuracy, yet they frequently misinterpret correlations as causal relationships, especially in the presence of confounding factors, dynamic workloads, and feedback loops. This study investigates how such failures arise by modeling tuning actions as interventions and analyzing the divergence between observed and true causal effects across multiple performance metrics. The results demonstrate that misleading performance gains are common under confounded conditions, leading to unstable and suboptimal configurations. It is further observed that these errors accumulate over time, reinforcing incorrect tuning strategies and degrading long-term system performance. By incorporating causal reasoning frameworks, the study provides a more accurate understanding of tuning effectiveness and highlights strategies for improving robustness in automated database optimization systems. The findings also emphasize the need for integrating causal validation mechanisms into real-time tuning pipelines to ensure consistent and reliable performance improvements.