Challenges and Opportunities with Causal Discovery Algorithms: Application to Alzheimer’s Pathophysiology

Authors

Xinpeng Shen, University of Minnesota Twin Cities
Sisi Ma, University of Minnesota Twin Cities
Prashanthi Vemuri, Mayo Clinic
Gyorgy Simon, University of Minnesota Twin Cities
Michael W. Weiner, University of California, San Francisco
Paul Aisen, University of Southern California
Ronald Petersen, Mayo Clinic
Clifford R. Jack, Mayo Clinic
Andrew J. Saykin, Indiana University Bloomington
William Jagust, University of California, Berkeley
John Q. Trojanowki, University of Pennsylvania
Arthur W. Toga, University of Southern California
Laurel Beckett, University of California, Davis
Robert C. Green, Harvard Medical School
John Morris, Washington University in St. Louis
Leslie M. Shaw, University of Pennsylvania
Zaven Khachaturian, Prevent Alzheimer’s Disease
Greg Sorensen, Siemens AG
Maria Carrillo, Alzheimer’s Association
Lew Kuller, University of Pittsburgh
Marc Raichle, Washington University in St. Louis
Steven Paul, Cornell University
Peter Davies, Albert Einstein College of Medicine of Yeshiva University
Howard Fillit, AD Drug Discovery Foundation
Franz Hefti, Acumen Pharmaceuticals
David Holtzman, Washington University in St. Louis
M. Marcel Mesulam, Northwestern University
William Potter, National Institute of Mental Health (NIMH)
Peter Snyder, Brown University
Adam Schwartz, Eli Lilly and Company
Tom Montine, University of Washington
Ronald G. Thomas, University of California, San Diego

Document Type

Article

Publication Date

12-1-2020

Journal

Scientific Reports

Volume

10

Issue

1

URL with Digital Object Identifier

10.1038/s41598-020-59669-x

Abstract

Causal Structure Discovery (CSD) is the problem of identifying causal relationships from large quantities of data through computational methods. With the limited ability of traditional association-based computational methods to discover causal relationships, CSD methodologies are gaining popularity. The goal of the study was to systematically examine whether (i) CSD methods can discover the known causal relationships from observational clinical data and (ii) to offer guidance to accurately discover known causal relationships. We used Alzheimer’s disease (AD), a complex progressive disease, as a model because the well-established evidence provides a “gold-standard” causal graph for evaluation. We evaluated two CSD methods, Fast Causal Inference (FCI) and Fast Greedy Equivalence Search (FGES) in their ability to discover this structure from data collected by the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We used structural equation models (which is not designed for CSD) as control. We applied these methods under three scenarios defined by increasing amounts of background knowledge provided to the methods. The methods were evaluated by comparing the resulting causal relationships with the “gold standard” graph that was constructed from literature. Dedicated CSD methods managed to discover graphs that nearly coincided with the gold standard. For best results, CSD algorithms should be used with longitudinal data providing as much prior knowledge as possible.