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Found 8 papers, 3 papers with code
Causal Discovery for fMRI data: Challenges, Solutions, and a Case Study
Designing studies that apply causal discovery requires navigating many researcher degrees of freedom.
Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow-Shrink Trees
However, the accuracy and execution time of learning algorithms generally struggle to scale to problems with hundreds of highly connected variables -- for instance, recovering brain networks from fMRI data.
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.
The m-connecting imset and factorization for ADMG models
The m-connecting imset and factorization criterion provide two new statistical tools for learning and inference with ADMG models.
Greedy Relaxations of the Sparsest Permutation Algorithm
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 Kohler and GSP of Solus, Wang and Uhler.
Learning Latent Causal Structures with a Redundant Input Neural Network
We developed a deep learning model, which we call a redundant input neural network (RINN), with a modified architecture and a regularized objective function to find causal relationships between input, hidden, and output variables.
FASK with Interventional Knowledge Recovers Edges from the Sachs Model
We report a procedure that, in one step from continuous data with minimal preparation, recovers the graph found by Sachs et al. \cite{sachs2005causal}, with only a few edges different.
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.
