FASK with Interventional Knowledge Recovers Edges from the Sachs Model

Abstract

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. The algorithm, Fast Adjacency Skewness (FASK), relies on a mixture of linear reasoning and reasoning from the skewness of variables; the Sachs data is a good candidate for this procedure since the skewness of the variables is quite pronounced. We review the ground truth model from Sachs et al. as well as some of the fluctuations seen in the protein abundances in the system, give the Sachs model and the FASK model, and perform a detailed comparison. Some variation in hyper-parameters is explored, though the main result uses values at or near the defaults learned from work modeling fMRI data.

arXiv comment13 pages, 21 figures, 2 tables, Technical Report

References in corpus (4)

• A Discovery Algorithm for Directed Cyclic Graphs
• Joint Causal Inference from Multiple Contexts
• Learning Large-Scale Bayesian Networks with the sparsebn Package
• Causal Generative Neural Networks

Cited by in corpus (4)

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• REX: Causal discovery based on machine learning and explainability techniques
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