AI RESEARCH
Fourier Feature Methods for Nonlinear Causal Discovery: FFML Scoring and FFCI Testing in Mixed Data
arXiv CS.LG
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ArXi:2605.05743v1 Announce Type: cross Gaussian process marginal likelihood scores and kernel conditional independence tests are theoretically appealing for nonlinear causal discovery but computationally prohibitive at scale. We present two complementary RFF-based methods forming a practical toolkit for score-based, constraint-based, and hybrid causal discovery.