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

TL;DR

This paper advocates for using causal inference frameworks in human-factor analysis to better understand intervention effects, demonstrated through a case study on post-occupancy evaluation data.

Contribution

It introduces a systematic approach to distinguish between descriptive and interventional questions, applying causal discovery to reveal causal relationships in human-centric systems.

Findings

Causal discovery uncovers intervention hierarchies missed by traditional analysis.

Explicit causal analysis improves understanding of intervention effects.

Method enhances decision-making in building science and ergonomics.

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.