Causal AI-Powered Event Interpretation: A Cause-and-Effect Discovery for Indoor Thermal Comfort Measurements

Abstract

Unexplainable indoor thermal comfort events from black-box models influence people to distrust suggestions from decision-support systems and ask for help from engineers and practitioners that are labor intensive and time consuming. These problems come from unknown cause and effect in the environments that cause the system not to produce explainable outcomes. This study proposes the cause-and-effect discovery for indoor thermal comfort events that help systems make human-like explanations to overcome these issues. The research contributions consist of three essential points. The first is perceptions based on the Internet of Things technologies that imitate human perception organs, which could sense signals as a system input component. The second is qualitative knowledge representation using random variable systems and graphs as the ground truth—the representation stores in the manner of human-like intelligence that people and systems can understand. The third is causal discovery algorithms that automatically determine the cause and effect in machine learning (ML) models from observational data. The results showed that models could discover cause-and-effect relationships close to the human-like intelligent-based model blueprint given observational data. They produce reasonable explanations for indoor thermal comfort events that help people trust such information and utilize it to make decisions.