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Causal Inference for Time series Analysis: Problems, Methods and Evaluation [article]

Raha Moraffah, Paras Sheth, Mansooreh Karami, Anchit Bhattacharya, Qianru Wang, Anique Tahir, Adrienne Raglin, Huan Liu
2021 arXiv   pre-print
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

Christopher Krich, Jakob Runge, Diego G. Miralles, Mirco Migliavacca, Oscar Perez-Priego, Tarek El-Madany, Arnaud Carrara, Miguel D. Mahecha
2020 Biogeosciences  
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?

Jakub Kořenek, Jaroslav Hlinka
2021 Entropy  
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

Christopher Krich, Jakob Runge, Diego G. Miralles, Mirco Migliavacca, Oscar Perez-Priego, Tarek El-Madany, Arnaud Carrara, Miguel D. Mahecha
2019 Biogeosciences Discussions  
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]

Ruocheng Guo, Lu Cheng, Jundong Li, P. Richard Hahn, Huan Liu
2019 arXiv   pre-print
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?

Sarah Friedrich, Gerd Antes, Sigrid Behr, Harald Binder, Werner Brannath, Florian Dumpert, Katja Ickstadt, Hans A. Kestler, Johannes Lederer, Heinz Leitgöb, Markus Pauly, Ansgar Steland (+2 others)
2021 Advances in Data Analysis and Classification  
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

D. L. González II, M. P. Angus, I. K. Tetteh, G. A. Bello, K. Padmanabhan, S. V. Pendse, S. Srinivas, J. Yu, F. Semazzi, V. Kumar, N. F. Samatova
2015 Nonlinear Processes in Geophysics  
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

Raouf Boutaba, Mohammad A. Salahuddin, Noura Limam, Sara Ayoubi, Nashid Shahriar, Felipe Estrada-Solano, Oscar M. Caicedo
2018 Journal of Internet Services and Applications  
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

D. L. González II, M. P. Angus, I. K. Tetteh, G. A. Bello, K. Padmanabhan, S. V. Pendse, S. Srinivas, J. Yu, F. Semazzi, V. Kumar, N. F. Samatova
2014 Nonlinear Processes in Geophysics Discussions  
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

Klaas E. Stephan, Sandra Iglesias, Jakob Heinzle, Andreea O. Diaconescu
2015 Neuron  
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

A. Mignan, G. Ouillon, D. Sornette, F. Freund
2021 The European Physical Journal Special Topics  
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]

Dan Stowell, Lisa Gill, David Clayton
2016 biorxiv/medrxiv   pre-print
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

Dan Stowell, Lisa Gill, David Clayton
2016 Journal of the Royal Society Interface  
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  
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]

Erik Dahlquist, Moksadur Rahman, Jan Skvaril, Konstantinos Kyprianidis
2021 AI and Learning Systems - Industrial Applications and Future Directions  
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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