Causal Machine Learning in IoT-based Engineering Problems: A Tool Comparison in the Case of Household Energy Consumption

Nikolaos-Lysias Kosioris, Sotiris Nikoletseas, Gabriel Filios, Stefanos H. Panagiotou

Keywords: Causal Machine Learning, Validation, Household Energy Consumption, PyWhy, CausalML, Tool Comparison, Machine learning for sciences, Social and economic aspects of machine learning

TL;DR: Ιntroduction to two prevalent causal machine learning tools and a practical application comparing the results of the tools.

Abstract:

The rapid increase in computing power and the ability to store Big Data in the infrastructure has enabled
predictions in a large variety of domains by Machine Learning. However, in many cases, existing Machine Learning tools are
considered insufficient or incorrect since they exploit only probabilistic dependencies rather than inference logic. Causal
Machine Learning methods seem to close this gap. In this paper, two prevalent tools based on Causal Machine Learning
methods are compared, as well as their mathematical underpinning background. The operation of the tools is demonstrated by
examining their response to 18 queries, based on the IDEAL Household Energy Dataset, published by the University of
Edinburgh. First, it was important to evaluate the causal relations assumption that allowed the use of this approach; this was
based on the preexisting scientific knowledge of the domain and was implemented by use of the in-built validation tools. Results
were encouraging and may easily be extended to other domains.

https://doi.org/10.48550/arXiv.2505.12147

Submission Number: 57