3 papers
cs.CE2026
Predicting Heterogeneous Treatment Effects Of Building Energy Saving Retrofits Using Causal Machine Learning
Kevin Zalipski, David Zapata Gonzalez, Oliver Müller
Information Systems research increasingly relies on machine learning (ML) to predict outcomes in complex sociotechnical systems, yet predictive models are not designed to identify…
cs.CE2026
A Step Towards Inherently Interpretable Causal Machine Learning Models For Decision Support
David Zapata Gonzalez
The growing reliance on machine learning for decisions across sectors underscores the importance of model transparency and interpretability. Existing post hoc explainability method…
cs.CE2025
Bridging the Gap Between Data-Driven And Theory-Driven Modelling - Leveraging Causal Machine Learning for Integrative Modelling of Dynamical Systems
David Zapata Gonzalez, Marcel Meyer, Oliver Mueller
Classical machine learning techniques often struggle with overfitting and unreliable predictions when exposed to novel conditions. Introducing causality into the modelling process…