Predicting Heterogeneous Treatment Effects Of Building Energy Saving Retrofits Using Causal Machine Learning

2026-07-02

AI Summary

This study addresses the challenge of selection bias—arising from household and building characteristics—that commonly biases causal effect estimates in traditional machine learning approaches to evaluating building energy retrofit impacts. To systematically assess the performance of state-of-the-art causal machine learning methods in estimating heterogeneous treatment effects, the authors develop the first simulation environment that integrates realistic physical mechanisms with empirically grounded adoption biases. Comparative experiments across multiple retrofit measures demonstrate that DoubleML consistently outperforms S-, T-, and X-Learner, achieving the lowest estimation error—particularly for complex envelope retrofits. These findings validate the efficacy of orthogonalization in mitigating confounding bias and establish a robust methodological foundation for large-scale energy efficiency policy evaluation.

Abstract

Information Systems research increasingly relies on machine learning (ML) to predict outcomes in complex sociotechnical systems, yet predictive models are not designed to identify causal effects. This limitation is particularly critical in building retrofits, where unbiased estimates of energy savings are essential for climate policy and investment decisions. Because retrofit adoption is shaped by household and building characteristics that also affect energy consumption, predictive ML can yield biased effect estimates. This paper systematically benchmarks leading causal ML estimators, including metalearners (S-, T- and X-Learners) and DoubleML across multiple retrofit interventions. To enable this comparison, we construct a physically grounded simulation in which true treatment effects and realistic adoption biases are known. Results show that DoubleML achieves the lowest estimation errors, particularly for complex envelope retrofits. These findings demonstrate that orthogonalising the treatment assignment improves causal effect estimation and provides a methodological foundation for large-scale energy retrofit and policy evaluation.

Problem

• heterogeneous treatment effects
• building energy saving retrofits
• causal machine learning
• energy consumption
• treatment effect estimation

Innovation

• Causal Machine Learning
• Heterogeneous Treatment Effects
• DoubleML
• Building Energy Retrofits
• Orthogonalization