Uncertainty Assessment and False Discovery Rate Control in High-Dimensional Granger Causal Inference

Aditya Chaudhry, Pan Xu, Quanquan Gu
Proceedings of the 34th International Conference on Machine Learning, PMLR 70:684-693, 2017.

Abstract

Causal inference among high-dimensional time series data proves an important research problem in many fields. While in the classical regime one often establishes causality among time series via a concept known as “Granger causality,” existing approaches for Granger causal inference in high-dimensional data lack the means to characterize the uncertainty associated with Granger causality estimates (e.g., p-values and confidence intervals). We make two contributions in this work. First, we introduce a novel asymptotically unbiased Granger causality estimator with corresponding test statistics and confidence intervals to allow, for the first time, uncertainty characterization in high-dimensional Granger causal inference. Second, we introduce a novel method for false discovery rate control that achieves higher power in multiple testing than existing techniques and that can cope with dependent test statistics and dependent observations. We corroborate our theoretical results with experiments on both synthetic data and real-world climatological data.

Cite this Paper

BibTeX
@InProceedings{pmlr-v70-chaudhry17a, title = {Uncertainty Assessment and False Discovery Rate Control in High-Dimensional {G}ranger Causal Inference}, author = {Aditya Chaudhry and Pan Xu and Quanquan Gu}, booktitle = {Proceedings of the 34th International Conference on Machine Learning}, pages = {684--693}, year = {2017}, editor = {Precup, Doina and Teh, Yee Whye}, volume = {70}, series = {Proceedings of Machine Learning Research}, month = {06--11 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v70/chaudhry17a/chaudhry17a.pdf}, url = {https://proceedings.mlr.press/v70/chaudhry17a.html}, abstract = {Causal inference among high-dimensional time series data proves an important research problem in many fields. While in the classical regime one often establishes causality among time series via a concept known as “Granger causality,” existing approaches for Granger causal inference in high-dimensional data lack the means to characterize the uncertainty associated with Granger causality estimates (e.g., p-values and confidence intervals). We make two contributions in this work. First, we introduce a novel asymptotically unbiased Granger causality estimator with corresponding test statistics and confidence intervals to allow, for the first time, uncertainty characterization in high-dimensional Granger causal inference. Second, we introduce a novel method for false discovery rate control that achieves higher power in multiple testing than existing techniques and that can cope with dependent test statistics and dependent observations. We corroborate our theoretical results with experiments on both synthetic data and real-world climatological data.} }
Endnote
%0 Conference Paper %T Uncertainty Assessment and False Discovery Rate Control in High-Dimensional Granger Causal Inference %A Aditya Chaudhry %A Pan Xu %A Quanquan Gu %B Proceedings of the 34th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2017 %E Doina Precup %E Yee Whye Teh %F pmlr-v70-chaudhry17a %I PMLR %P 684--693 %U https://proceedings.mlr.press/v70/chaudhry17a.html %V 70 %X Causal inference among high-dimensional time series data proves an important research problem in many fields. While in the classical regime one often establishes causality among time series via a concept known as “Granger causality,” existing approaches for Granger causal inference in high-dimensional data lack the means to characterize the uncertainty associated with Granger causality estimates (e.g., p-values and confidence intervals). We make two contributions in this work. First, we introduce a novel asymptotically unbiased Granger causality estimator with corresponding test statistics and confidence intervals to allow, for the first time, uncertainty characterization in high-dimensional Granger causal inference. Second, we introduce a novel method for false discovery rate control that achieves higher power in multiple testing than existing techniques and that can cope with dependent test statistics and dependent observations. We corroborate our theoretical results with experiments on both synthetic data and real-world climatological data.
APA
Chaudhry, A., Xu, P. & Gu, Q.. (2017). Uncertainty Assessment and False Discovery Rate Control in High-Dimensional Granger Causal Inference. Proceedings of the 34th International Conference on Machine Learning, in Proceedings of Machine Learning Research 70:684-693 Available from https://proceedings.mlr.press/v70/chaudhry17a.html.

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