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We present two complementary RFF-based methods forming a practical toolkit for score-based, constraint-based, and hybrid causal discovery.\n  The Fourier Feature Marginal Likelihood (FFML) score approximates the exact GP marginal likelihood by replacing the n x n kernel Gram matrix with a finite-dimensional feature representation, reducing cost to O(nm^2 + m^3) while retaining the probabilistic interpretation and automatic complexity penalty of the exact score. FFML extends to mixed (continuous + discrete) parent sets via a product-kernel construction, with a Kronecker path for small discrete parent sets and a Hadamard-product path otherwise.\n  The Fourier Feature Conditional Independence (FFCI) test is a fast nonparametric CI test for mixed data. 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