# Causal Explanation of Concept Drift -- A Truly Actionable Approach

arXiv: https://arxiv.org/abs/2507.23389 · HTML: https://mlsanity.com/paper/2507.23389/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 29 references that are in the map.
- First posted on arXiv 31 Jul 2025.

## Builds on (most cited first, 10 of 10)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [A survey on datasets for fairness-aware machine learning](https://mlsanity.com/paper/2110.00530/) (2021)
- [Causal-learn: Causal Discovery in Python](https://mlsanity.com/paper/2307.16405/) (2023)
- [Model Based Explanations of Concept Drift](https://mlsanity.com/paper/2303.09331/) (2023)
- [Understanding Concept Drift](https://mlsanity.com/paper/1704.00362/) (2017)
- [Modeling Time-evolving Causality over Data Streams](https://mlsanity.com/paper/2502.08963/) (2025)
- [Localizing Anomalies in Critical Infrastructure using Model-Based Drift Explanations](https://mlsanity.com/paper/2310.15830/) (2023)
- [Utilizing Concept Drift for Measuring the Effectiveness of Policy Interventions: The Case of the COVID-19 Pandemic](https://mlsanity.com/paper/2012.03728/) (2020)
- [Feature Relevance Analysis to Explain Concept Drift -- A Case Study in Human Activity Recognition](https://mlsanity.com/paper/2301.08453/) (2023)

## Abstract (arXiv)

In a world that constantly changes, it is crucial to understand how those changes impact different systems, such as industrial manufacturing or critical infrastructure. Explaining critical changes, referred to as concept drift in the field of machine learning, is the first step towards enabling targeted interventions to avoid or correct model failures, as well as malfunctions and errors in the physical world. Therefore, in this work, we extend model-based drift explanations towards causal explanations, which increases the actionability of the provided explanations. We evaluate our explanation strategy on a number of use cases, demonstrating the practical usefulness of our framework, which isolates the causally relevant features impacted by concept drift and, thus, allows for targeted intervention.