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Found 9 papers, 3 papers with code
Py-Tetrad and RPy-Tetrad: A New Python Interface with R Support for Tetrad Causal Search
With the addition of some simple tools and the provision of working examples for both Python and R, using JPype and Reticulate to interface Python and R with Tetrad is straightforward and intuitive.
Improving Accuracy of Permutation DAG Search using Best Order Score Search
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 theoretically.
Identification of Effective Connectivity Subregions
These algorithms allow for identification of subregions of voxels driving the connectivity between regions of interest, recovering valuable anatomical and functional information that is lost when ROIs are aggregated.
A Comparison of Public Causal Search Packages on Linear, Gaussian Data with No Latent Variables
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 to the extent possible from data generated recursively from linear, Gaussian structure equation models (SEMs) with no latent variables, for random graphs, with no additional knowledge of variable order or adjacency structure, and without additional specification of intervention information.
Mixed Graphical Models for Causal Analysis of Multi-modal Variables
Graphical causal models are an important tool for knowledge discovery because they can represent both the causal relations between variables and the multivariate probability distributions over the data.
algcomparison: Comparing the Performance of Graphical Structure Learning Algorithms with TETRAD
In this report we describe a tool for comparing the performance of graphical causal structure learning algorithms implemented in the TETRAD freeware suite of causal analysis methods.
Scaling up Greedy Causal Search for Continuous Variables
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 inappropriate for big data problems without sacrificing accuracy.
Effects of Nonparanormal Transform on PC and GES Search Accuracies
Liu, et al., 2009 developed a transformation of a class of non-Gaussian univariate distributions into Gaussian distributions.
A Scalable Conditional Independence Test for Nonlinear, Non-Gaussian Data
Among these, perhaps the most efficient has been KCI (Kernel Conditional Independence, Zhang et al. (2011)), with computational requirements that grow effectively at least as O(N3), placing it out of range of large sample size analysis, and restricting its applicability to high dimensional data sets.
