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Constraint-based causal discovery relies on conditional independence tests (CITs), which are highly unstable in high-dimensional settings. As the conditioning set grows, CITs suffer from low statistical power, leading to frequent false negatives. In the context of structure learning, this causes error propagation that degrades the accuracy of the estimated graph. We propose DF-PC (Deduce-First PC), a theoretically sound framework that integrates graphoid-based reasoning into the PC algorithm. Unlike prior approaches that primarily utilize deduction for conflict resolution or additive checks, DF-PC adopts a proactive ``Deduce-First'' strategy: it prioritizes logical deduction from strictly lower-order tests to preemptively replace high-order CITs. This "Deduce-First" strategy enables structure learning with potentially fewer CITs while improving performance. Empirical evaluations across various settings demonstrate that DF-PC achieves competitive learning performance and computational efficiency.