On Causality Inference in Time Series
نویسندگان
چکیده
Causality discovery has been one of the core tasks in scientific research since the beginning of human scientific history. In the age of data tsunami, the task could involve millions of variables, which cannot be achieved feasibly by human. However, the causal discovery using artificial intelligence and statistical techniques in non-experimental settings faces several challenges. In this work, we address three practical challenges regarding Granger causality, one of the most popular causality inference techniques for time series data. First, we analyze the consistency of two most popular Granger causality techniques and show that the significance test is not consistent in high dimensions. Second, we review the nonparametric generalization of the Lasso-Granger technique called Generalized Lasso Granger (GLG) to uncover Granger causality relationships among irregularly sampled time series. Finally, we describe two techniques to uncover the casual dependence in non-linear datasets. Extensive experiments on the climate datasets are provided to show the significant advantages of the proposed algorithms over their state-of-the-art counterparts.
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