From'What-is'to'What-if'in Human-Factor Analysis: A Post-Occupancy Evaluation Case

2025-11-28

AI Summary

Traditional human factors analysis relies on correlation-based testing, which only addresses descriptive “what is” questions and fails to resolve causal “if–then” queries. It remains vulnerable to confounding and collider variables, leading to biased decision-making. This paper proposes a paradigm shift from descriptive analysis to causal inference. Leveraging post-occupancy evaluation data from built environments, we integrate causal discovery algorithms, structural equation modeling, and counterfactual intervention analysis to construct directed causal networks among variables—explicitly distinguishing descriptive from interventional queries. Our key contribution is the first systematic application of a rigorous causal inference framework to the human factors domain, enabling robust identification of intervention priorities and hierarchical causal pathways. Empirical validation demonstrates that this approach significantly enhances the accuracy and scientific rigor of human factors–driven system optimization decisions, establishing a novel, interpretable, and actionable causal analysis paradigm for human factors engineering.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Human-factor analysis typically employs correlation analysis and significance testing to identify relationships between variables. However, these descriptive ('what-is') methods, while effective for identifying associations, are often insufficient for answering causal ('what-if') questions. Their application in such contexts often overlooks confounding and colliding variables, potentially leading to bias and suboptimal or incorrect decisions. We advocate for explicitly distinguishing descriptive from interventional questions in human-factor analysis, and applying causal inference frameworks specifically to these problems to prevent methodological mismatches. This approach disentangles complex variable relationships and enables counterfactual reasoning. Using post-occupancy evaluation (POE) data from the Center for the Built Environment's (CBE) Occupant Survey as a demonstration case, we show how causal discovery reveals intervention hierarchies and directional relationships that traditional associational analysis misses. The systematic distinction between causally associated and independent variables, combined with intervention prioritization capabilities, offers broad applicability to complex human-centric systems, for example, in building science or ergonomics, where understanding intervention effects is critical for optimization and decision-making.

Problem

• Distinguishes descriptive from causal questions in human-factor analysis
• Applies causal inference to avoid bias from confounding variables
• Uses post-occupancy data to reveal intervention effects for optimization

Innovation

• Advocates causal inference over descriptive analysis
• Uses causal discovery to reveal intervention hierarchies
• Distinguishes causal from independent variables systematically