对比两种因果机器学习工具，提升家庭能耗预测的逻辑可靠性。
Causal Machine Learning in IoT-based Engineering Problems: A Tool Comparison in the Case of Household Energy Consumption
- 用因果推理替代传统概率模型，改进能耗预测逻辑
- 在18个查询上验证工具表现，结果支持方法有效性
- 适合关注预测可解释性的智能物联网研究者
计算能力提升与大数据存储使机器学习广泛应用于各类领域。然而，许多场景中现有方法因仅依赖概率关联而非因果推理而被认为不充分或错误。因果机器学习有望弥补这一缺口。本文比较了两种主流因果机器学习工具及其数学基础，通过爱丁堡大学发布的IDEAL家庭能耗数据集，对18个查询进行测试。首先基于领域科学知识验证因果假设，并利用内置验证工具完成。结果令人鼓舞，且可推广至其他领域。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。