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

TL;DR

This paper compares two causal machine learning tools applied to household energy consumption data, highlighting their mathematical foundations, validation process, and effectiveness in inferring causal relations from observational data.

Contribution

It provides a comparative analysis of two causal ML tools, demonstrating their application and validation in the domain of household energy consumption.

Findings

Both tools successfully inferred causal relations consistent with domain knowledge.

Validation tools effectively assessed the causal assumptions in the dataset.

Results suggest potential for extending causal ML methods to other domains.

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.