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Causal Explanation of Concept Drift -- A Truly Actionable Approach

David Komnick, Kathrin Lammers, Barbara Hammer, Valerie Vaquet, Fabian Hinder

TL;DR

The paper tackles concept drift by embedding drift explanations in a causal framework using time as an experimental feature and the do-calculus to derive drift-reversing and conditional interventions. It formalizes a causal model over time, enabling identification of drift-relevant features that can be targeted to reverse or mitigate drift effects. Through semi-synthetic experiments built on real datasets and PC-based causal discovery, the framework demonstrates that drift is typically driven by a small set of features directly influenced by time, yielding actionable explanations even when causal graphs are imperfect. The approach offers a principled path to monitor, explain, and mitigate drift in time-evolving systems, with potential impact on critical infrastructure monitoring and automated remediation.

Abstract

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.

Causal Explanation of Concept Drift -- A Truly Actionable Approach

TL;DR

The paper tackles concept drift by embedding drift explanations in a causal framework using time as an experimental feature and the do-calculus to derive drift-reversing and conditional interventions. It formalizes a causal model over time, enabling identification of drift-relevant features that can be targeted to reverse or mitigate drift effects. Through semi-synthetic experiments built on real datasets and PC-based causal discovery, the framework demonstrates that drift is typically driven by a small set of features directly influenced by time, yielding actionable explanations even when causal graphs are imperfect. The approach offers a principled path to monitor, explain, and mitigate drift in time-evolving systems, with potential impact on critical infrastructure monitoring and automated remediation.

Abstract

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.

Paper Structure

This paper contains 16 sections, 4 theorems, 23 equations, 4 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. Concept Drift
  3. Related Work
  4. Computational Causality
  5. Causal Explanations of Concept Drift
  6. A Causal Model over Time
  7. First Analysis and Limitations
  8. A Refined Approach
  9. Empirical Evaluation
  10. Datasets and experimental setup
  11. Preliminary stability analysis
  12. Experimental results
  13. Conclusion
  14. Acknowledgment.
  15. Disclosure of Interests.
  16. ...and 1 more sections

Key Result

Theorem 1

Let ${\mathcal{X}}$ be a dataspace with features $\mathcal{F}$, ${\mathcal{T}}$ be a time domain, and $(P_T,{\mathcal{D}}_t)$ be a distribution process. Let $E$ be experiment with intervention on ${\mathcal{X}} \times {\mathcal{T}}$ that has the holistic distribution of ${\mathcal{D}}_t$ as the dist $P_T$-a.s. In particular, the presence of drift is equivalent to time-interventions affecting the d

Figures (4)

  • Figure 1: Simple causal model (A) with time ($T$) controlled sprinkler ( origin=c]225emoji/shower.png ) which also switches off if it rains (emoji/cloud-with-rain.png); in both cases the ground will be wet (emoji/sweat-droplets.png). If we manually turn off the sprinkler (B), i.e., apply a $\textnormal{do}$-operator, all connections to its parents are removed in the graphical model.
  • Figure 2: Performance of the PC Algorithm. Black edges indicate the ground truth, while green edges indicate detections by the PC algorithm; thickness correlates with the number of runs in which an edge was detected.
  • Figure 3: Case Studies on the Adult dataset. Black edges indicate the ground truth, while green edges indicate detections by the PC algorithm; thickness correlates with the number of runs in which an edge was detected. Children of $T$ are marked green, with the thickness of the border indicating the number of runs that correctly identified this relationship.
  • Figure 4: Case Studies on the Student dataset. Black edges indicate the ground truth, while green edges indicate detections by the PC algorithm; thickness correlates with the number of runs in which an edge was detected. Children of $T$ are marked green, with the thickness of the border indicating the number of runs that correctly identified this relationship.

Theorems & Definitions (9)

  • Definition 1
  • Definition 2: Experiment with Interventions
  • Definition 3
  • Definition 4: Causeless set of features
  • Theorem 1
  • Definition 5: Drift-Reversing Intervention
  • Lemma 1
  • Theorem 2
  • Theorem 3