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CauseMap: fast inference of causality from complex time series.

Maher MC, Hernandez RD - PeerJ (2015)

Bottom Line: 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 systems theory that requires only mild assumptions.Conclusions.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Epidemiology and Biostatistics, University of California , San Francisco, CA , USA.

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 systems theory that requires only mild assumptions. This theorem allows us to reconstruct high dimensional system dynamics using a time series of only a single variable. These reconstructions can be thought of as shadows of the true causal system. If reconstructed shadows can predict points from opposing time series, we can infer that the corresponding variables are providing views of the same causal system, and so are causally related. Unlike traditional metrics, this test can establish the directionality of causation, even in the presence of feedback loops. Furthermore, since CCM can extract causal relationships from times series of, e.g., a single individual, it may be a valuable tool to personalized medicine. We implement CCM in Julia, a high-performance programming language designed for facile technical computing. Our software package, CauseMap, is platform-independent and freely available as an official Julia package. Conclusions. CauseMap is an efficient implementation of a state-of-the-art algorithm for detecting causality from time series data. We believe this tool will be a valuable resource for biomedical research and personalized medicine.

No MeSH data available.


Related in: MedlinePlus

The effect of time series length on ρccm convergence.Black, blue, and red lines illustrate ρccm for the full, 1/2 thinned, and 1/3 thinned datasets, respectively. For a given color, darker lines show ρccm for the test of whether Didinium abundance influences Paramecium abundance. Lighter lines examine the converse.
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fig-2: The effect of time series length on ρccm convergence.Black, blue, and red lines illustrate ρccm for the full, 1/2 thinned, and 1/3 thinned datasets, respectively. For a given color, darker lines show ρccm for the test of whether Didinium abundance influences Paramecium abundance. Lighter lines examine the converse.

Mentions: CauseMap is designed to examine causal relationships in time series with 25 or more observations. In order to illustrate the effects of shorter time series, we thinned the Paramedium-Didinium data set by one-half and by one-third, yielding series of 30 and 20 observations, respectively. Figure 2 demonstrates the effect of this reduction on the convergence of predictive skill (ρccm). We see that the 1/2 thinned data set recapitulates the trends observed in the full series, including the relative magnitudes of ρccm between the mappings of Didinium to Paramecium and vice versa. The 1/3 thinned sample set, on the other hand, no longer demonstrates convergence. In addition, compared to the longer sets, it exhibits the opposite trend in relative predictive skill between the two mappings. Patterns in max ρccm versus E and τp are approximately conserved, however (Fig. S1).


CauseMap: fast inference of causality from complex time series.

Maher MC, Hernandez RD - PeerJ (2015)

The effect of time series length on ρccm convergence.Black, blue, and red lines illustrate ρccm for the full, 1/2 thinned, and 1/3 thinned datasets, respectively. For a given color, darker lines show ρccm for the test of whether Didinium abundance influences Paramecium abundance. Lighter lines examine the converse.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4359046&req=5

fig-2: The effect of time series length on ρccm convergence.Black, blue, and red lines illustrate ρccm for the full, 1/2 thinned, and 1/3 thinned datasets, respectively. For a given color, darker lines show ρccm for the test of whether Didinium abundance influences Paramecium abundance. Lighter lines examine the converse.
Mentions: CauseMap is designed to examine causal relationships in time series with 25 or more observations. In order to illustrate the effects of shorter time series, we thinned the Paramedium-Didinium data set by one-half and by one-third, yielding series of 30 and 20 observations, respectively. Figure 2 demonstrates the effect of this reduction on the convergence of predictive skill (ρccm). We see that the 1/2 thinned data set recapitulates the trends observed in the full series, including the relative magnitudes of ρccm between the mappings of Didinium to Paramecium and vice versa. The 1/3 thinned sample set, on the other hand, no longer demonstrates convergence. In addition, compared to the longer sets, it exhibits the opposite trend in relative predictive skill between the two mappings. Patterns in max ρccm versus E and τp are approximately conserved, however (Fig. S1).

Bottom Line: 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 systems theory that requires only mild assumptions.Conclusions.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Epidemiology and Biostatistics, University of California , San Francisco, CA , USA.

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 systems theory that requires only mild assumptions. This theorem allows us to reconstruct high dimensional system dynamics using a time series of only a single variable. These reconstructions can be thought of as shadows of the true causal system. If reconstructed shadows can predict points from opposing time series, we can infer that the corresponding variables are providing views of the same causal system, and so are causally related. Unlike traditional metrics, this test can establish the directionality of causation, even in the presence of feedback loops. Furthermore, since CCM can extract causal relationships from times series of, e.g., a single individual, it may be a valuable tool to personalized medicine. We implement CCM in Julia, a high-performance programming language designed for facile technical computing. Our software package, CauseMap, is platform-independent and freely available as an official Julia package. Conclusions. CauseMap is an efficient implementation of a state-of-the-art algorithm for detecting causality from time series data. We believe this tool will be a valuable resource for biomedical research and personalized medicine.

No MeSH data available.


Related in: MedlinePlus