Roa-Henriquez, A.
, Fung, J.
, Abudu, R.
, Helgeson, J.
and Thomas, D.
(2026),
Causal Machine Learning: An Empirical Approach to Supply Chain Management, Digital Transformation of Supply Chain: Emerging Technologies and Business Trends, Springer International Series in Operations Research & Management Science, Cham, , [online], https://doi.org/10.1007/978-3-032-01218-0, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=959143
(Accessed February 28, 2026)
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Causal Machine Learning: An Empirical Approach to Supply Chain Management
Published
Author(s)
Alfredo Roa-Henriquez, , Ruhaimatu Abudu, ,
Abstract
Over the past two decades, artificial intelligence (AI) has revolutionized industries, with machine learning (ML) at its core. While ML has enhanced supply chain management (SCM) in efficiency and resilience, it often relies on correlations, risking decisions based on spurious relationships. Causal machine learning (CML) offers a solution by focusing on cause-and-effect relationships, promising accuracy and better decision-making. Leading companies like Amazon and Walmart are beginning to harness CML for predictive modeling and AI-driven pricing strategies. Despite its potential, CML in SCM remains nascent, with limited empirical research. This chapter delves into the integration of causal inference in SCM, emphasizing methods that avoid pre-selecting covariates. It reviews foundational causal estimation techniques, surveys CML algorithms, and showcases their application through a simulation. By connecting theoretical advancements with practical implementations, the chapter underscores CML's transformative potential for SCM research and practice, and outlines future directions for its development.Citation
Digital Transformation of Supply Chain: Emerging Technologies and Business Trends
Publisher Info
Springer International Series in Operations Research & Management Science, Cham,
Pub Type
Book Chapters
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Created January 9, 2026, Updated February 27, 2026