document: Temporal causal modeling with graphical granger methods The need for mining causality , beyond mere statistical correlations , for real world problems has been recognized widely . Many of these applications naturally involve temporal data , which raises the challenge of how best to leverage the temporal information for causal modeling . Recently graphical modeling with the concept of `` Granger causality '' , based on the intuition that a cause helps predict its effects in the future , has gained attention in many domains involving time series data analysis . With the surge of interest in model selection methodologies for regression , such as the Lasso , as practical alternatives to solving structural learning of graphical models , the question arises whether and how to combine these two notions into a practically viable approach for temporal causal modeling . In this paper , we examine a host of related algorithms that , loosely speaking , fall under the category of graphical Granger methods , and characterize their relative performance from multiple viewpoints . Our experiments show , for instance , that the Lasso algorithm exhibits consistent gain over the canonical pairwise graphical Granger method . We also characterize conditions under which these variants of graphical Granger methods perform well in comparison to other benchmark methods . Finally , we apply these methods to a real world data set involving key performance indicators of corporations , and present some concrete results .
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