Deep Causal Learning to Explain and Quantify The Geo-Tension's Impact on Natural Gas Market

TL;DR

This paper introduces a deep learning approach to quantify the impact of geopolitical shocks, like the Russia-Ukraine war, on the German natural gas market using causal inference techniques.

Contribution

It applies deep neural network-based Granger causality to identify demand drivers and constructs counterfactual scenarios to measure the war's impact on energy sectors.

Findings

Quantifies the effect of the Russia-Ukraine war on natural gas demand.

Identifies key drivers influencing natural gas prices.

Provides a framework for counterfactual analysis in energy markets.

Abstract

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.