AuthorsV. Naumova, K. Hlavackova-Schindler and S. V. Pereverzyev
EditorsW. Wiedermann and A. von Eye
TitleGranger Causality for Ill-Posed Problems: Ideas, Methods, and Application in Life Sciences
AfilliationScientific Computing, Center for Biomedical Computing (SFF)
Publication TypeBook Chapter
Year of Publication2015
Book TitleStatistics and Causality: Methods for Applied Empirical Research
PublisherJohn Wiley & Sons Limited Wiley
Place PublishedWest Sussex, United Kingdom

Granger causality, based on a vector autoregressive model, is one of the most popular methods for uncovering the temporal dependencies between time series. The application of Granger causality to detect inference among a big number of variables (genes) requires a variable selection procedure. To fight with lack of informative data, the so called regularization procedures are applied. In this chapter, we review current literature applying Granger causality with Lasso regularization technique for ill-posed problems. We discuss regularization procedures for inverse and ill-posed problems and present our recent approaches that are evaluated in a case study of gene regulatory networks reconstruction.

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