A Step Towards Inherently Interpretable Causal Machine Learning Models For Decision Support
D.R. Zapata Gonzalez, in: ECIS Conference, AIS, 2026.

Predicting Heterogeneous Treatment Effects Of Building Energy Saving Retrofits Using Causal Machine Learning
K. Zalipski, D.R. Zapata Gonzalez, O. Müller, in: 2026.

Temporal causal feature selection for robust machine learning modelling of data center operations
D.R. Zapata Gonzalez, M. Meyer, K. Zalipski, O. Müller, Applied Energy 417 (2026).

Benchmarking Time Series Foundation Models for Short-Term Household Electricity Load Forecasting
M. Meyer, D.R. Zapata Gonzalez, S.B. Kaltenpoth, O. Müller, IEEE Access 13 (2025) 218141–218153.

Process Mining for Robust Heat Integration through Process- and Product-Centric Energy Demand Modeling
D.R. Zapata Gonzalez, K. Brennig, K. Benkert, F. Schlosser, O. Müller, in: ACM SIGEnergy Energy Informatics Review, Association for Computing Machinery (ACM), 2025, pp. 19–31.

Causal Machine Learning Approaches for Modelling Data Center Heat Recovery: A Physical Testbed Study
D.R. Zapata Gonzalez, M. Meyer, O. Müller, in: ACM SIGEnergy Energy Informatics Review, Association for Computing Machinery (ACM), 2025, pp. 4–10.

Bridging the gap between data-driven and theory-driven modelling – leveraging causal machine learning for integrative modelling of dynamical systems
D.R. Zapata Gonzalez, M. Meyer, O. Müller, in: 2025.