Table 1. .
Summary for all the methods discussed in this paper. GC: Granger causality; SEM: Structural Equation Modeling; DC: Dynamic Causal Modeling; LN: LINGaM; BN: Bayesian Nets; TE: Transfer Entropy; PW-LR: Pairwise Likelihood Ratios; net: network-wise; dag: Directed Acyclic Graphs only; pw: pairwise; +/−: depends on implementation; mc: model comparison; c: classical hypothesis testing; ml: machine learning; l: low; h: high; n/a: nonapplicable. PW-LR is based on the same concept as Patel’s tau (PT), and the inference is the same, therefore we did not add a separate column for PT.
| Feature — Method | GC | SEM | DCM | LN | BN | TE | PW-LR |
|---|---|---|---|---|---|---|---|
| Group of methods | net | net | net | dag | dag | net | pw |
| Sign of connections | + | + | + | + | − | + | − |
| Directionality | + | + | + | − | − | + | + |
| Connection strength | + | + | + | + | + | + | + |
| Immediacy | +/− | +/− | − | + | + | +/− | + |
| Resilience to confounds | +/− | +/− | − | +/− | +/− | +/− | + |
| Causality through… | c | mc/c | mc | ml+c | mc/ml | c | c |
| Computational cost | l | l/h | h | h | l/h | l | l |
| Model-free? | − | − | − | − | + | + | + |
| Prespecify the graph? | − | − | + | − | +/− | − | − |
| Regression in time | + | − | − | − | − | + | − |