Applying Causal Machine Learning to Spatiotemporal Data Analysis: An Investigation of Opportunities and Challenges
Auteur(s): Mulomba Mukendi Christian, KIKETA MOFUNDA VOGEL, KUTANGILA M. DAVID, MAMPUYA H. K. PESCIE, MUKENZE N. JUNIOR, TASHO TASHEV, KASUNZI M. LANDRY, KYANDOGHERE KYAMAKYA, KASEREKA K. SELAINNom de la revue/Journal: IEEE Access
Mois: août
Volume: 13
Année: 2025
pages: 141832-141857
Résumé
<p>Traditional spatiotemporal data analysis often relies on predictive models that overlook causal relationships, making it difficult to identify true drivers and formulate effective interventions. To bridge this gap, we review causal machine learning (CML) techniques for spatiotemporal data, aiming to provide robust insights into their unique advantages. Our literature review reveals that fewer than 1% of studies in major databases explicitly integrate CML with spatiotemporal analysis. After rigorous screening, we analyze 51 relevant papers, categorizing their contributions into four key areas (totaling 62 methodological approaches due to multi-category papers): 1) causal effect discovery and estimation (32 approaches), 2) prediction accuracy enhancement (19), 3) pattern recognition limitations (10), and 4) interpretability (1). This distribution highlights a critical research gap, particularly in interpretability and comprehensive frameworks. We further examine unique challenges in spatiotemporal data, such as spatial autocorrelation and temporal dependencies, that complicate causal inference but also present opportunities for innovation. Promising approaches include the synergy of spatiotemporal Granger causality and structural equation modeling with spatial lags, which capture complex interdependencies while preserving interpretability. Future directions include developing interpretable causal models, advancing real-time causal inference in dynamic environments, and addressing computational challenges (scalability, efficiency, and complexityinterpretability trade-offs). We also discuss ethical considerations, such as bias mitigation in causal discovery and societal implications of spatiotemporal causal inference. By synthesizing challenges and opportunities, this work advances the application of CML in spatiotemporal analysis, with implications for climate science, economics, epidemiology, and urban planning.</p>