Deep Causal Learning to Explain and Quantify The Geo-Tension's Impact on Natural Gas Market
This work addresses the challenge of quantifying geopolitical impacts on energy markets for policymakers and analysts, though it is incremental as it builds on existing causal methods.
The paper tackled the problem of assessing the impact of geopolitical shocks like the Russian-Ukrainian war on natural gas demand by applying deep neural network-based Granger causality to identify key drivers and construct counterfactual scenarios, resulting in quantifiable estimates of the shock's effect on German energy sectors.
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.