Probabilistic Causal Modeling of Barriers to Accessibility for Persons with Disabilities in Canada

Abstract

This paper utilizes a methodological two-step process incorporating statistical and causal probabilistic modeling techniques to investigate factors affecting the accessibility experiences of persons with disabilities in Canada. We deploy a network-based approach using empirical data to perform a holistic assessment of the relations between various demographic features (e.g., age, gender and type of disability) and accessibility barriers. A statistical measurement method is applied that utilizes structural equation modeling supported by exploratory factor analysis. For causal probabilistic modeling, Bayesian networks are employed as a straightforward and compact way to interpret knowledge representation. This causal reasoning approach analyzes the nature and frequency of encountering barriers based on data to understand the risk factors contributing to pressing accessibility issues. Furthermore, to evaluate network performance and overcome any data limitations, synthetic data generation techniques are applied to create and validate artificial data built on real-world knowledge. The proposed framework strives to provide reasoning to understand the prevalence of physical, social, communication or technological barriers encountered by persons with disabilities in their daily lives. This study contributes to the identification of areas for prioritization in facilitating accessibility regulation and practices to realize an inclusive society.