# What if? Causal Machine Learning in Supply Chain Risk Management

arXiv: https://arxiv.org/abs/2408.13556 · HTML: https://mlsanity.com/paper/2408.13556/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 73 references that are in the map.
- First posted on arXiv 24 Aug 2024.

## Builds on (most cited first, 11 of 11)

- [Causal Machine Learning: A Survey and Open Problems](https://mlsanity.com/paper/2206.15475/) (2022)
- [Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation](https://mlsanity.com/paper/2003.03934/) (2020)
- [DoubleML -- An Object-Oriented Implementation of Double Machine Learning in Python](https://mlsanity.com/paper/2104.03220/) (2021)
- [Long Story Short: Omitted Variable Bias in Causal Machine Learning](https://mlsanity.com/paper/2112.13398/) (2021)
- [DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions](https://mlsanity.com/paper/2108.13518/) (2021)
- [DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R](https://mlsanity.com/paper/2103.09603/) (2021)
- [On Implementing Autonomous Supply Chains: a Multi-Agent System Approach](https://mlsanity.com/paper/2310.09435/) (2023)
- [Root Cause Attribution of Delivery Risks via Causal Discovery with Reinforcement Learning](https://mlsanity.com/paper/2408.05860/) (2024)
- [Causally Learning an Optimal Rework Policy](https://mlsanity.com/paper/2306.04223/) (2023)
- [Selecting Data Adaptive Learner from Multiple Deep Learners using Bayesian Networks](https://mlsanity.com/paper/2008.07709/) (2020)
- [Trustworthy, responsible, ethical AI in manufacturing and supply chains: synthesis and emerging research questions](https://mlsanity.com/paper/2305.11581/) (2023)

## Built on by (most cited first, 1 of 1)

- [Generating Logically Consistent Synthetic Supply Chain Data with LLM-Driven Knowledge Graph Reasoning](https://mlsanity.com/paper/2605.26823/) (2026)

## Abstract (arXiv)

The penultimate goal for developing machine learning models in supply chain management is to make optimal interventions. However, most machine learning models identify correlations in data rather than inferring causation, making it difficult to systematically plan for better outcomes. In this article, we propose and evaluate the use of causal machine learning for developing supply chain risk intervention models, and demonstrate its use with a case study in supply chain risk management in the maritime engineering sector. Our findings highlight that causal machine learning enhances decision-making processes by identifying changes that can be achieved under different supply chain interventions, allowing "what-if" scenario planning. We therefore propose different machine learning developmental pathways for for predicting risk, and planning for interventions to minimise risk and outline key steps for supply chain researchers to explore causal machine learning.