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2007
ACM

Temporal causal modeling with graphical granger methods

10 years 11 months ago
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 multip...
Andrew Arnold, Yan Liu, Naoki Abe
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2007
Where KDD
Authors Andrew Arnold, Yan Liu, Naoki Abe
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