Comparison of strategies for scalable causal discovery of latent variable models from mixed data

International Journal of Data Science and Analytics
Vineet K RaghuPanayiotis V Benos

Abstract

Modern technologies allow large, complex biomedical datasets to be collected from patient cohorts. These datasets are comprised of both continuous and categorical data ("Mixed Data"), and essential variables may be unobserved in this data due to the complex nature of biomedical phenomena. Causal inference algorithms can identify important relationships from biomedical data; however, handling the challenges of causal inference over mixed data with unmeasured confounders in a scalable way is still an open problem. Despite recent advances into causal discovery strategies that could potentially handle these challenges; individually, no study currently exists that comprehensively compares these approaches in this setting. In this paper, we present a comparative study that addresses this problem by comparing the accuracy and efficiency of different strategies in large, mixed datasets with latent confounders. We experiment with two extensions of the Fast Causal Inference algorithm: a maximum probability search procedure we recently developed to identify causal orientations more accurately, and a strategy which quickly eliminates unlikely adjacencies in order to achieve scalability to high-dimensional data. We demonstrate that these me...Continue Reading

References

Jun 28, 2011·BMC Cancer·Frederik N EngsigNiels Obel
Jun 19, 2015·Journal of Computational and Graphical Statistics : a Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America·Jason D Lee, Trevor J Hastie
Jul 8, 2015·PLoS Biology·Zachary D StephensGene E Robinson
Jun 15, 2016·BMC Bioinformatics·Andrew J SedgewickPanayiotis V Benos
Jul 19, 2016·IEEE/ACM Transactions on Computational Biology and Bioinformatics·Thuc Duy LeShu Hu
Jan 6, 2017·PloS One·Karine RissoPierre-Marie Roger

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Citations

Dec 25, 2019·Bioinformatics·Kristina L BuschurPanayiotis V Benos
May 12, 2020·Nucleic Acids Research·Xiaoyu GePanayiotis V Benos
Jun 1, 2021·Pediatric Critical Care Medicine : a Journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies·Vineet K RaghuAlicia K Au

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Software Mentioned

MATLAB
FCI
MGM
Stable
MAX
CFCI

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