  Natural gas demand is a crucial factor for predicting natural gas prices and
thus has a direct influence on the power system. However, existing methods face
challenges in assessing the impact of shocks, such as the outbreak of the
Russian-Ukrainian war. In this context, we apply deep neural network-based
Granger causality to identify important drivers of natural gas demand.
Furthermore, the resulting dependencies are used to construct a counterfactual
case without the outbreak of the war, providing a quantifiable estimate of the
overall effect of the shock on various German energy sectors. The code and
dataset are available at https://github.com/bonaldli/CausalEnergy.
