Name
Abstract | ||
|---|---|---|
We introduce the concepts of weak and strong asymmetries in multivariate time series in the context of causal modeling. Weak asymmetries are by definition differences in univariate properties of the data, which are not necessarily related to causal relationships between time series. Nevertheless, they might still mislead (in particular Granger-) causal analyses. We propose two general strategies to overcome the negative influence of weak asymmetries in causal modeling. One is to assess the confidence of causal predictions using the antisymmetry-symmetry ratio, while the other one is based on comparing the result of a causal analysis to that of an equivalent analysis of time-reversed data. We demonstrate that Granger Causality applied to the SiSEC challenge on causal analysis of simulated EEG data greatly benefits from our suggestions. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-28551-6_4 | LVA/ICA |
Keywords | Field | DocType |
causal prediction,causal modeling,multivariate time series,granger causality,weak asymmetry,time-reversed data,particular granger,causal analysis,simulated eeg data,granger-causal analysis,equivalent analysis,weak data asymmetry | Causal analysis,Econometrics,Multivariate statistics,Granger causality,Eeg data,Univariate,Statistics,Mathematics,Causal model | Conference |
Citations | PageRank | References |
9 | 0.66 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stefan Haufe | 1 | 645 | 36.63 |
| Vadim V Nikulin | 2 | 325 | 27.80 |
| G Nolte | 3 | 535 | 50.42 |