Introducing causal inference in the energy-efficient building design process

TL;DR

This paper introduces a causal inference framework to improve decision support in energy-efficient building design by identifying parametric dependencies, integrating knowledge and data, and enhancing interpretability.

Contribution

It presents a novel four-step causal inference process for discovering and analyzing parametric dependencies, bridging domain knowledge with data-driven methods in building design.

Findings

Effective causal diagrams can be identified with interventions

The framework improves interpretability of design decisions

Simulation results validate the proposed estimators

Abstract

"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.