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stat.ML2026
Fourier Feature Methods for Nonlinear Causal Discovery: FFML Scoring, TRFF Scoring, and FFCI Testing in Mixed Data
Joseph D. Ramsey
Gaussian process (GP) marginal likelihood scores and kernel conditional independence tests are theoretically appealing for nonlinear causal discovery but computationally prohibitiv…
stat.ML2025
Scalable Causal Discovery from Recursive Nonlinear Data via Truncated Basis Function Scores and Tests
Joseph Ramsey, Bryan Andrews, Peter Spirtes
Learning graphical conditional independence structures from nonlinear, continuous or mixed data is a central challenge in machine learning and the sciences, and many existing metho…