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(granger_causality)=
# Granger Causality

Granger Causality (GC) is a statistical concept of causality based on prediction.
If a time series X "Granger-causes" a time series Y, then past values of X should contain information that helps predict Y beyond the information contained in past values of Y alone
{cite:p}`zaninAssessingGrangerCausality2021`.
Before using Granger Causality, it's important to detrend the time series data to avoid spurious results, i.e. using the Z-Score.

```python
import delaynet as dn

# Calculate single-lag Granger Causality
result = dn.connectivity(ts1, ts2, metric="granger causality", lag_steps=5)
```

Parameters:
- `lag_steps`: Time lags to consider. An integer will consider lags [1, ..., lag_steps]. Passing a list will consider the specified values as lags.

```{eval-rst}
.. automethod:: delaynet.connectivities.granger.gt_multi_lag
    :noindex:
```