What question did this study set out to answer?

This research aims to integrate causal discovery with predictive modeling to enhance oil price forecasting accuracy.

May 22, 2026

Causal Machine Learning in Commodity Markets: A Framework for Oil Price Forecasting

Key Points

This research aims to integrate causal discovery with predictive modeling to enhance oil price forecasting accuracy.
Applied the PC algorithm for causal graph identification from diverse market data.
Constrained feature selection in the LightGBM model to focus on causally-relevant variables.
Utilized SHAP analysis for interpretability and counterfactual reasoning to evaluate results.
The approach maintained forecasting accuracy with causally-informed feature selection.
Causal structure supported established economic principles related to inventory dynamics and regional arbitrage.
Demonstrated interpretability benefits through SHAP analysis.

Abstract

This paper introduces a modeling framework that integrates constraint-based causal discovery with predictive algorithms for oil market analysis. The methodology first applies the PC algorithm to identify a causal graph from heterogeneous market data. This graph then informs feature selection for a LightGBM model, constraining it to causally-relevant variables. Empirical results demonstrate that this approach maintains forecasting accuracy while providing interpretability through SHAP analysis and counterfactual reasoning. The derived causal structure corroborates established economic principles, highlighting inventory dynamics and regional arbitrage as primary price drivers.

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