Fourier Feature Methods for Nonlinear Causal Discovery: FFML Scoring and FFCI Testing in Mixed Data
📰 ArXiv cs.AI
Learn to apply Fourier Feature Methods for nonlinear causal discovery in mixed data using FFML scoring and FFCI testing, enabling efficient and scalable causal analysis
Action Steps
- Apply FFML scoring to approximate GP marginal likelihood in mixed data
- Use FFCI testing for kernel conditional independence in nonlinear causal discovery
- Implement RFF-based methods for score-based and constraint-based causal discovery
- Evaluate the performance of FFML and FFCI methods on benchmark datasets
- Integrate FFML and FFCI into existing causal discovery pipelines for improved efficiency
Who Needs to Know This
Data scientists and researchers working on causal discovery problems can benefit from this method to improve the efficiency and scalability of their analyses, particularly when dealing with mixed data
Key Insight
💡 Fourier Feature Methods provide a practical toolkit for nonlinear causal discovery, offering a scalable alternative to traditional Gaussian process and kernel-based methods
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🚀 Accelerate nonlinear causal discovery with Fourier Feature Methods! 📊 FFML scoring and FFCI testing enable efficient analysis of mixed data 📈
Key Takeaways
Learn to apply Fourier Feature Methods for nonlinear causal discovery in mixed data using FFML scoring and FFCI testing, enabling efficient and scalable causal analysis
Full Article
Title: Fourier Feature Methods for Nonlinear Causal Discovery: FFML Scoring and FFCI Testing in Mixed Data
Abstract:
arXiv:2605.05743v1 Announce Type: cross Abstract: 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. The Fourier Feature Marginal Likelihood (FFML) score approximates the exact GP marginal likelihood by replacing the n x n ke
Abstract:
arXiv:2605.05743v1 Announce Type: cross Abstract: 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. The Fourier Feature Marginal Likelihood (FFML) score approximates the exact GP marginal likelihood by replacing the n x n ke
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