# Introducing causal inference in the energy-efficient building design process

arXiv: https://arxiv.org/abs/2203.10115 · HTML: https://mlsanity.com/paper/2203.10115/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2022.

## Built on by (most cited first, 7 of 7)

- [Combining Physics-based and Data-driven Modeling for Building Energy Systems](https://mlsanity.com/paper/2411.01055/) (2024)
- [Utilizing Domain Knowledge: Robust Machine Learning for Building Energy Prediction with Small, Inconsistent Datasets](https://mlsanity.com/paper/2302.10784/) (2023)
- [Pathway toward prior knowledge-integrated machine learning in engineering](https://mlsanity.com/paper/2307.06950/) (2023)
- [Using causal inference to avoid fallouts in data-driven parametric analysis: a case study in the architecture, engineering, and construction industry](https://mlsanity.com/paper/2309.11509/) (2023)
- [Machine Learning in Proton Exchange Membrane Water Electrolysis -- Part I: A Knowledge-Integrated Framework](https://mlsanity.com/paper/2404.03660/) (2024)
- [Causal Machine Learning in IoT-based Engineering Problems: A Tool Comparison in the Case of Household Energy Consumption](https://mlsanity.com/paper/2505.12147/) (2025)
- [From 'What-is' to 'What-if' in Human-Factor Analysis: A Post-Occupancy Evaluation Case](https://mlsanity.com/paper/2512.02060/) (2025)

## Abstract (arXiv)

"What-if" questions are intuitively generated and commonly asked during the design process. Engineers and architects need to inherently conduct design decisions, progressing from one phase to another. They either use empirical domain experience, simulations, or data-driven methods to acquire consequential feedback. We take an example from an interdisciplinary domain of energy-efficient building design to argue that the current methods for decision support have limitations or deficiencies in four aspects: parametric independency identification, gaps in integrating knowledge-based and data-driven approaches, less explicit model interpretation, and ambiguous decision support boundaries. In this study, we first clarify the nature of dynamic experience in individuals and constant principal knowledge in design. Subsequently, we introduce causal inference into the domain. A four-step process is proposed to discover and analyze parametric dependencies in a mathematically rigorous and computationally efficient manner by identifying the causal diagram with interventions. The causal diagram provides a nexus for integrating domain knowledge with data-driven methods, providing interpretability and testability against the domain experience within the design space. Extracting causal structures from the data is close to the nature design reasoning process. As an illustration, we applied the properties of the proposed estimators through simulations. The paper concludes with a feasibility study demonstrating the proposed framework's realization.