Causal Machine Learning: An Empirical Approach to Supply Chain Management

Alfredo Roa-Henriquez (), Juan Fung, Ruhaimatu Abudu, Jennifer Helgeson and Douglas Thomas
Additional contact information
Alfredo Roa-Henriquez: North Dakota State University (NDSU), College of Business
Juan Fung: National Institute of Standards and Technology (NIST), Applied Economics Office (AEO), Engineering Lab
Ruhaimatu Abudu: North Dakota State University (NDSU), College of Business
Jennifer Helgeson: National Institute of Standards and Technology (NIST), Applied Economics Office (AEO), Engineering Lab
Douglas Thomas: National Institute of Standards and Technology (NIST), Applied Economics Office (AEO), Engineering Lab

Chapter Chapter 13 in Emerging Technologies in Supply Chains, 2026, pp 327-358 from Springer

Abstract: 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.

Keywords: Supply chain management; Causal machine learning; Predictive analytics; Decision-making (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-032-01218-0_13

Ordering information: This item can be ordered from
http://www.springer.com/9783032012180

DOI: 10.1007/978-3-032-01218-0_13

Access Statistics for this chapter

More chapters in International Series in Operations Research & Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().