Counterfactual Explanations Under Concept Drift

2026-05-17

AI Summary

This work addresses the challenge of maintaining counterfactual explanations (CFEs) under concept drift, a scenario where existing methods lack efficient update mechanisms. It introduces, for the first time, CFE maintenance in data stream settings and proposes a lightweight, model-agnostic updating strategy. By locally sampling around the original instance, the method dynamically repairs CFEs to preserve both validity and plausibility without requiring full regeneration, while ensuring proximity to the original input. Empirical evaluations on synthetic drifting data streams demonstrate that the approach consistently sustains CFE validity and local plausibility over time, achieving significantly lower computational overhead compared to naive re-generation baselines.

Abstract

Counterfactual explanations (CFEs) provide actionable recourse, but most methods assume a static framework with fixed data and a trained classifier. This assumption breaks in evolving data environments, such as data streams, where online models are repeatedly updated under concept drift. We identify CFE maintenance in this setting as a previously overlooked problem: explanations that are valid when generated may silently become invalid as the model evolves, including robust CFEs, which are not designed for continuous drift. We propose a lightweight, model-agnostic update scheme that repairs existing CFEs using local sampling to estimate validity and plausibility directions while preserving proximity to the original instance. Experiments on synthetic drifting streams show that initially created CFEs rapidly lose validity, whereas maintained CFEs preserve validity and local plausibility at a lower cost than repeated regeneration.

Problem

• counterfactual explanations
• concept drift
• model updating
• explanation validity
• data streams

Innovation

• counterfactual explanations
• concept drift
• model-agnostic
• explanation maintenance
• local sampling