Navigating Time's Possibilities: Plausible Counterfactual Explanations for Multivariate Time-Series Forecast through Genetic Algorithms

2026-02-28

AI Summary

This study addresses the opacity of causal mechanisms and the lack of counterfactual explanations in multivariate time series forecasting. To this end, it proposes a novel framework that integrates genetic algorithms with Granger causality testing, augmented by quantile regression to model the conditional distribution under interventions. This approach uniquely combines evolutionary search with rigorous causal inference to automatically generate statistically significant and interpretable counterfactual scenarios. Experiments on real-world datasets demonstrate that the proposed method not only effectively uncovers the underlying causal structures among complex variables but also enables reliable reasoning about the outcomes of hypothetical interventions, thereby providing actionable counterfactual support for decision-making.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Counterfactual learning has become promising for understanding and modeling causality in complex and dynamic systems. This paper presents a novel method for counterfactual learning in the context of multivariate time series analysis and forecast. The primary objective is to uncover hidden causal relationships and identify potential interventions to achieve desired outcomes. The proposed methodology integrates genetic algorithms and rigorous causality tests to infer and validate counterfactual dependencies within temporal sequences. More specifically, we employ Granger causality to enhance the reliability of identified causal relationships, rigorously assessing their statistical significance. Then, genetic algorithms, in conjunction with quantile regression, are used to exploit these intricate causal relationships to project future scenarios. The synergy between genetic algorithms and causality tests ensures a thorough exploration of the temporal dynamics present in the data, revealing hidden dependencies and enabling the projection of outcomes under hypothetical interventions. We evaluate the performance of our algorithm on real-world data, showcasing its ability to handle complex causal relationships, revealing meaningful counterfactual insights, and allowing for the prediction of outcomes under hypothetical interventions.

Problem

• counterfactual explanations
• multivariate time-series
• causal relationships
• hypothetical interventions
• time-series forecasting

Innovation

• counterfactual learning
• genetic algorithms
• Granger causality
• multivariate time series
• quantile regression