56 citations · 316 across the 22 of their papers we have counts for
10 papers · 1 filter
Greedy Relaxations of the Sparsest Permutation Algorithm
Wai-Yin Lam, Bryan Andrews, Joseph Ramsey
There has been an increasing interest in methods that exploit permutation reasoning to search for directed acyclic causal models, including the "Ordering Search" of Teyssier and Ko…
Improving Accuracy of Permutation DAG Search using Best Order Score Search
Joseph D. Ramsey
The Sparsest Permutation (SP) algorithm is accurate but limited to about 9 variables in practice; the Greedy Sparest Permutation (GSP) algorithm is faster but less weak theoretical…
A Comparison of Public Causal Search Packages on Linear, Gaussian Data with No Latent Variables
Joseph D. Ramsey, Bryan Andrews
We compare Tetrad (Java) algorithms to the other public software packages BNT (Bayes Net Toolbox, Matlab), pcalg (R), bnlearn (R) on the \vanilla" task of recovering DAG structure…
Mixed Graphical Models for Causal Analysis of Multi-modal Variables
Andrew J Sedgewick, Joseph D. Ramsey, Peter Spirtes +2
Graphical causal models are an important tool for knowledge discovery because they can represent both the causal relations between variables and the multivariate probability distri…
Improving Accuracy and Scalability of the PC Algorithm by Maximizing P-value
Joseph Ramsey
A number of attempts have been made to improve accuracy and/or scalability of the PC (Peter and Clark) algorithm, some well known (Buhlmann, et al., 2010; Kalisch and Buhlmann, 200…
Scaling up Greedy Causal Search for Continuous Variables
Joseph D. Ramsey
As standardly implemented in R or the Tetrad program, causal search algorithms used most widely or effectively by scientists have severe dimensionality constraints that make them i…
Effects of Nonparanormal Transform on PC and GES Search Accuracies
Joseph D. Ramsey
Liu, et al., 2009 developed a transformation of a class of non-Gaussian univariate distributions into Gaussian distributions. Liu and collaborators (2012) subsequently applied the…
A Scalable Conditional Independence Test for Nonlinear, Non-Gaussian Data
Joseph D. Ramsey
Many relations of scientific interest are nonlinear, and even in linear systems distributions are often non-Gaussian, for example in fMRI BOLD data. A class of search procedures fo…
Adjacency-Faithfulness and Conservative Causal Inference
Joseph Ramsey, Jiji Zhang, Peter L. Spirtes
Most causal inference algorithms in the literature (e.g., Pearl (2000), Spirtes et al. (2000), Heckerman et al. (1999)) exploit an assumption usually referred to as the causal Fait…
Discovering Cyclic Causal Models by Independent Components Analysis
Gustavo Lacerda, Peter L. Spirtes, Joseph Ramsey +1
We generalize Shimizu et al's (2006) ICA-based approach for discovering linear non-Gaussian acyclic (LiNGAM) Structural Equation Models (SEMs) from causally sufficient, continuous-…