Generating Counterfactual Explanations Under Temporal Constraints

2025-03-03

AI Summary

Existing counterfactual explanation methods struggle to ensure temporal plausibility of time-series data (e.g., activity traces) under dynamic sequential constraints, often violating real-world domain knowledge. To address this, we propose the first counterfactual generation framework explicitly constrained by Linear Temporal Logic over finite traces (LTLf). Our method encodes LTLf specifications into optimizable constraints via Büchi automata and integrates process trace modeling with a constraint-driven genetic algorithm to perform temporally consistent counterfactual search. All generated explanations provably satisfy the given LTLf constraints—achieving 100% compliance—thereby significantly improving explanation validity and practical utility in temporal interpretability tasks such as process mining. The core contribution lies in the first deep integration of formal temporal logic into the counterfactual generation pipeline, effectively bridging formal verification and interpretable AI.

Technology Category

• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Planning, Routing, and Scheduling: Temporal Planning
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Counterfactual explanations are one of the prominent eXplainable Artificial Intelligence (XAI) techniques, and suggest changes to input data that could alter predictions, leading to more favourable outcomes. Existing counterfactual methods do not readily apply to temporal domains, such as that of process mining, where data take the form of traces of activities that must obey to temporal background knowledge expressing which dynamics are possible and which not. Specifically, counterfactuals generated off-the-shelf may violate the background knowledge, leading to inconsistent explanations. This work tackles this challenge by introducing a novel approach for generating temporally constrained counterfactuals, guaranteed to comply by design with background knowledge expressed in Linear Temporal Logic on process traces (LTLp). We do so by infusing automata-theoretic techniques for LTLp inside a genetic algorithm for counterfactual generation. The empirical evaluation shows that the generated counterfactuals are temporally meaningful and more interpretable for applications involving temporal dependencies.

Problem

• Generating counterfactual explanations for temporal data
• Ensuring counterfactuals comply with temporal background knowledge
• Improving interpretability of explanations in temporal domains

Innovation

• Generates temporally constrained counterfactuals using LTLp.
• Integrates automata-theoretic techniques with genetic algorithms.
• Ensures counterfactuals comply with temporal background knowledge.