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Causal Inference for Time series Analysis: Problems, Methods and Evaluation
[article]
2021
arXiv
pre-print
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Time series data has been also used to study the effect of interventions over time. ...
Estimating the effect of an intervention and identifying the causal relations from the data can be performed via causal inference. ...
Similarly, in the presence of hidden confounders, Liu et al. [101] proposed Deep Sequential Weighting (DSW) for estimating the ITE with time-varying confounders. ...
arXiv:2102.05829v1
fatcat:ako4ja7rnzfhhk2dtukct4x2tm
Estimating causal networks in biosphere–atmosphere interaction with the PCMCI approach
2020
Biogeosciences
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Overall, the results confirm the capacity of the causal discovery method to extract time-lagged linear dependencies under realistic settings. ...
Secondly, we explore global Normalised Difference Vegetation Index time series (GIMMS 3g), along with gridded climate data to study large-scale climatic drivers of vegetation greenness. ...
The authors affiliated with the Max Planck Institute for Biogeochemistry thank the European Space Agency for funding the "Earth System Data Lab" project. ...
doi:10.5194/bg-17-1033-2020
fatcat:6jzaamivbjenlkccwzhv5clxua
Causality in Reversed Time Series: Reversed or Conserved?
2021
Entropy
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systems where the inferred causal direction appears unchanged under time reversal. ...
We start with a theoretical analysis that demonstrates that a perfect coupling reversal under time reversal occurs only under very specific conditions, followed up by constructing low-dimensional examples ...
Data Availability Statement: Example data presented in this paper are available upon request by email to the authors. ...
doi:10.3390/e23081067
fatcat:2o5ro7cwgve2fi3r2fpr7uyswi
Causal networks of biosphere–atmosphere interactions
2019
Biogeosciences Discussions
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Understanding these links is an essential prerequisite for predicting the Earth system under climate change conditions. ...
Secondly, we analyse global NDVI time series (GIMMS 3g) along with gridded climate data to study large-scale climatic drivers of vegetation greenness. ...
Here we tested PCMCI, an algorithm that estimates causal graphs from empirical time-series. ...
doi:10.5194/bg-2019-297
fatcat:nmevp5fddrevdm2uqi372ucl6m
A Survey of Learning Causality with Data: Problems and Methods
[article]
2019
arXiv
pre-print
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After that, we discuss the connections between learning causality and machine learning. At the end, some open problems are presented to show the great potential of learning causality with data. ...
The era of big data provides researchers with convenient access to copious data. However, people often have little knowledge about it. ...
Furthermore, for learning causal relationship in time series data, hidden time series acts like unobserved confounders in i.i.d. data. Confounding bias can lead to faulty causal conclusions [133] . ...
arXiv:1809.09337v3
fatcat:zq5hmgg345haxir2b6poq36yla
Is there a role for statistics in artificial intelligence?
2021
Advances in Data Analysis and Classification
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With its specialist knowledge of data evaluation, starting with the precise formulation of the research question and passing through a study design stage on to analysis and interpretation of the results ...
collection, differentiation of causality and associations and assessment of uncertainty in results. ...
Acknowledgements We would like to thank Rolf Biehler for his valuable input on Data Science projects at schools. Moreover, Willi Sauerbrei (University Freiburg) and Kaspar Rufibach (F. ...
doi:10.1007/s11634-021-00455-6
fatcat:xwuoyg6l3jcejcvqfsmhrvpzvm
On the data-driven inference of modulatory networks in climate science: an application to West African rainfall
2015
Nonlinear Processes in Geophysics
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to obtain a consensus result from the application of such varied methodologies. ...
These relationships fall into two categories: well-known associations from prior climate knowledge, such as the relationship with the El Niño–Southern Oscillation (ENSO) and putative links, such as North ...
This causal inference algorithm should not assume causal sufficiency or acyclicity of the causal structure (Hyttinen et al., 2013) , since latent variables (i.e., confounders) and feedback loops are ubiquitous ...
doi:10.5194/npg-22-33-2015
fatcat:p7rrxz3axbambi4ab3xzvkejtu
A comprehensive survey on machine learning for networking: evolution, applications and research opportunities
2018
Journal of Internet Services and Applications
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Machine Learning (ML) has been enjoying an unprecedented surge in applications that solve problems and enable automation in diverse domains. ...
Therefore, this is a timely contribution of the implications of ML for networking, that is pushing the barriers of autonomic network operation and management. ...
In packet time series, OCNM flags 26 out of 34 anomalies but generates 14 FPs, while KOAD gives different TP and FP under different parameters. ...
doi:10.1186/s13174-018-0087-2
fatcat:jvwpewceevev3n4keoswqlcacu
On the data-driven inference of modulatory networks in climate science: an application to West African rainfall
2014
Nonlinear Processes in Geophysics Discussions
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to obtain a consensus result from the application of such varied methodologies. ...
We applied coupled heterogeneous association rule mining (CHARM), Lasso multivariate regression, and Dynamic Bayesian networks to find relationships within a complex system, and explored means with which ...
This causal inference algorithm should not assume causal sufficiency or acyclicity of the causal structure (Hyttinen et al., 2013) , since latent variables (i.e., confounders) and feedback loops are ubiquitous ...
doi:10.5194/npgd-1-479-2014
fatcat:5f76hszljff7tpgjclym4rsuyu
Translational Perspectives for Computational Neuroimaging
2015
Neuron
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This article reviews contemporary frameworks for computational neuroimaging, with a focus on forward models linking unobservable brain states to measurements. ...
Focusing on schizophrenia as a paradigmatic spectrum disease, we review applications of these models to psychiatric questions, identify methodological challenges, and highlight trends of convergence among ...
estimates of an initial pathophysiological state as an ''anchor'' for subsequent disease dynamics expressed by biochemical and behavioral time series. ...
doi:10.1016/j.neuron.2015.07.008
pmid:26291157
fatcat:kexghlnh5jdoteeptmb36pggre
Global Earthquake Forecasting System (GEFS): The challenges ahead
2021
The European Physical Journal Special Topics
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The most encouraging results are obtained for ground-based geoelectric signals, although the probability gain is likely small compared to an earthquake clustering baseline. ...
familiar with such types of investigations. ...
The authors are grateful to Y. Kamer and J. Scoville for their valuable comments on an earlier version of this manuscript. ...
doi:10.1140/epjst/e2020-000261-8
fatcat:cbchnj4bjnf7lauypghwpipkya
Detailed temporal structure of communication networks in groups of songbirds
[article]
2016
biorxiv/medrxiv
pre-print
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This has advantages over cross-correlation analysis in that it can correctly handle common-cause confounds and provides a generative model of call patterns with explicit parameters for the influences between ...
Further, a fitted model can be used to generate novel synthetic call sequences. We apply the method to calls recorded from groups of domesticated zebra finch (Taenopyggia guttata) individuals. ...
Thanks also to Maeve McMahon for lots of assistance with the zebra finch recording study. ...
doi:10.1101/039370
fatcat:avpt365zerdxjei2xpaixzm3si
Detailed temporal structure of communication networks in groups of songbirds
2016
Journal of the Royal Society Interface
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This has advantages over cross-correlation analysis in that it can correctly handle common-cause confounds and provides a generative model of call patterns with explicit parameters for the influences between ...
Further, a fitted model can be used to generate novel synthetic call sequences. We apply the method to calls recorded from groups of domesticated zebra finch (Taeniopygia guttata) individuals. ...
Thanks also to Maeve McMahon for lots of assistance with the zebra finch recording study. ...
doi:10.1098/rsif.2016.0296
pmid:27335223
pmcid:PMC4938092
fatcat:55iq5h4mf5a4pdhwiasl22agxu
Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine
2020
Database: The Journal of Biological Databases and Curation
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To implement effective precision medicine with enhanced ability to positively impact patient outcomes and provide real-time decision support, it is important to harness the power of electronic health records ...
social issues related to the privacy and protection of healthcare data with effective balance. ...
We would like to give special thanks to Dr. Christopher Bonin for providing editorial support. ...
doi:10.1093/database/baaa010
pmid:32185396
pmcid:PMC7078068
fatcat:ypsuz5dewvcgtpjx4vjkhi545q
AI Overview: Methods and Structures
[chapter]
2021
AI and Learning Systems - Industrial Applications and Future Directions
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In this chapter we try to give an overview of a number of methods, and how they can be utilized in process industry applications. ...
Models are then used for many different applications like output prediction, soft sensors, fault detection, diagnostics, decision support, classifications, process optimization, model predictive control ...
Acknowledgements This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 723523.
Author details ...
doi:10.5772/intechopen.90741
fatcat:2nikzaeoajbkfos6a32ny22xzy
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