Causal ML for fair energy policy interventions: estimating impact heterogeneity of insulation programs via do-calculus

D'Amico, Bernardino

Abstract

Modern machine learning excels at pattern recognition but often fails to support decision-making, as it cannot distinguish correlation from causation. This is a critical limitation in high-stakes domains, where relying on statistical associations can reproduce historical biases embedded in the data. To address this, we apply do-calculus within a causal Bayesian network (CBN) framework to estimate the effect of residential energy-efficiency interventions (specifically, external wall insulation) on household gas consumption. By encoding structural assumptions in a directed acyclic graph, we derive post-intervention distributions from observational data, disentangling causal identification from statistical inference. This enables estimation of both average and subgroup-specific treatment effects, revealing substantial behavioural heterogeneity: households under high energy burden show significantly smaller energy savings post-intervention. Ultimately, this work illustrates how causal ML can address the biases and limitations of predictive models, and how formal tools like do-calculus can transform ML systems into more robust instruments for policy and decision-making under uncertainty.

Citation

D'Amico, B. (2025, September). Causal ML for fair energy policy interventions: estimating impact heterogeneity of insulation programs via do-calculus. Presented at 24th UK Workshop on Computational Intelligence (UKCI 2025), Edinburgh

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 24th UK Workshop on Computational Intelligence (UKCI 2025)
• Start Date: Sep 3, 2025
• End Date: Sep 5, 2025
• Acceptance Date: Jul 1, 2025
• Deposit Date: Aug 1, 2025
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Series Title: Advances in Intelligent Systems and Computing
• Keywords: Causal inference, Do-calculus, Graphical models, Directed acyclic graphs
• Publisher URL: https://link.springer.com/series/11156
• External URL: https://ukci2025.napier.ac.uk/