CauseMap: fast inference of causality from complex time series

PeerJ
M Cyrus Maher, Ryan D Hernandez

Abstract

Background. Establishing health-related causal relationships is a central pursuit in biomedical research. Yet, the interdependent non-linearity of biological systems renders causal dynamics laborious and at times impractical to disentangle. This pursuit is further impeded by the dearth of time series that are sufficiently long to observe and understand recurrent patterns of flux. However, as data generation costs plummet and technologies like wearable devices democratize data collection, we anticipate a coming surge in the availability of biomedically-relevant time series data. Given the life-saving potential of these burgeoning resources, it is critical to invest in the development of open source software tools that are capable of drawing meaningful insight from vast amounts of time series data. Results. Here we present CauseMap, the first open source implementation of convergent cross mapping (CCM), a method for establishing causality from long time series data (≳25 observations). Compared to existing time series methods, CCM has the advantage of being model-free and robust to unmeasured confounding that could otherwise induce spurious associations. CCM builds on Takens' Theorem, a well-established result from dynamical syste...Continue Reading

References

Jan 1, 2009·The Annals of Applied Statistics·Michael R Kosorok
Apr 13, 2011·PloS One·Ethan R Deyle, George Sugihara
Sep 22, 2012·Science·George SugiharaStephan Munch
Feb 7, 2013·Trends in Neurosciences·Jane A Foster, Karen-Anne McVey Neufeld
Jul 12, 2013·Science Translational Medicine·Ivan Vujkovic-CvijinJoseph M McCune
Sep 25, 2014·Frontiers in Immunology·Marie-Claire ArrietaBrett Finlay

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Citations

Aug 3, 2018·Chaos·Subhradeep Roy, Benjamin Jantzen

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Software Mentioned

R
CauseMap
matplotlib
Python
CCM
Julia

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