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Comparison of Strategies for Scalable Causal Discovery of Latent Variable Models from Mixed Data

DocUID: 2018-007 Full Text: PDF

Author: Vineet K. Raghu, Joseph D. Ramsey, Alison Morris, Dimitrios V. Manatakis, Peter Spirtes, Panos K. Chrysanthis, Clark Glymour, Panayiotis 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 methods significantly outperform the state of the art in the field by achieving both accurate edge orientations and tractable running time in simulation experiments on datasets with up to 500 variables. Finally, we demonstrate the usability of the best performing approach on real data by applying it to a biomedical dataset of HIV-infected individuals.

Published In: International Journal of Data Science and Analytics

ISBN: 2364-415X (Print ISSN), 2364-4168 (Online ISSN)

Volume: 6Number: 1Pages: 33-45

Year Published: 2018

DOI: https://doi.org/10.1007/s41060-018-0104-3

Project: CausalMGM Subject Area: Data Exploration, Data Mining, Biomedical Informatics, Data Integration

Publication Type: Journal Paper

Sponsor: T32CA082084, NIH U01HL137159

Citation:Text Latex BibTex XML Vineet K. Raghu, Joseph D. Ramsey, Alison Morris, Dimitrios V. Manatakis, Peter Spirtes, Panos K. Chrysanthis, Clark Glymour, and Panayiotis V. Benos. Comparison of Strategies for Scalable Causal Discovery of Latent Variable Models from Mixed Data. International Journal of Data Science and Analytics. 6(1):33-45. 2018. DOI: https://doi.org/10.1007/s41060-018-0104-3.