Modern compilers like LLVM and GCC provide hundreds of optimization options (e.g., flags or passes), yet their fixed, predefined sequences (e.g., -O3) often fail to exploit the full performance potential of specific programs. Search-based auto-tuning has emerged to address this pass selection and ordering problem — known as the phase ordering problem. Existing approaches typically reduce search complexity by identifying critical flags or employing localized group-based mutations. However, these methods often remain feature-agnostic or rely on manually designed static mappings during the online search. Consequently, they fail to adaptively link program features to optimization logic. Furthermore, they are prone to being misled by spurious correlations and noise inherent in sparse performance data.

To address this limitation, we propose CausalTuner, a feature-aware framework that injects causal rules into a Two-Phase Rule-Injected Search Engine to guide the optimization search. By identifying performance-critical pass subsequences and mining the causal dependencies between program features and optimization effectiveness, CausalTuner effectively transforms the search process from heuristic-driven to causal-guided. We evaluate CausalTuner on a diverse range of benchmarks, including cBench, Polybench, SPEC CPU 2017, and llama.cpp. CausalTuner consistently outperforms existing autotuning methods, discovering superior optimization configurations more efficiently with fewer search iterations.