7 citations · 7 across the 5 of their papers we have counts for
5 papers
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…
Efficient Latent Variable Causal Discovery: Combining Score Search and Targeted Testing
Joseph Ramsey, Bryan Andrews, Peter Spirtes
Learning causal structure from observational data is especially challenging when latent variables or selection bias are present. The Fast Causal Inference (FCI) algorithm addresses…
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…
Choosing DAG Models Using Markov and Minimal Edge Count in the Absence of Ground Truth
Joseph D. Ramsey, Bryan Andrews, Peter Spirtes
We give a novel nonparametric pointwise consistent statistical test (the Markov Checker) of the Markov condition for directed acyclic graph (DAG) or completed partially directed ac…
Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow-Shrink Trees
Bryan Andrews, Joseph Ramsey, Ruben Sanchez-Romero +2
Learning graphical conditional independence structures is an important machine learning problem and a cornerstone of causal discovery. However, the accuracy and execution time of l…