Asset Details
Do you wish to reserve the book?
Causal Machine Learning in Commodity Markets: A Framework for Oil Price Forecasting
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Causal Machine Learning in Commodity Markets: A Framework for Oil Price Forecasting
Do you wish to request the book?
Causal Machine Learning in Commodity Markets: A Framework for Oil Price Forecasting
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Causal Machine Learning in Commodity Markets: A Framework for Oil Price Forecasting
2026
Overview
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.
Publisher
EDP Sciences
