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

Abstract

Highlights: This research investigates the causal relationship between accessibility barriers and demographic and other traits of people with disabilities based on the Canadian Disability Survey 2022. We apply a unique computational intelligence tool—probabilistic causal models such as structural equation modeling and Bayesian networks—to discover those relationships and model scenarios to assess the risks and barriers in accessing services, transportation and communications in the context of inclusive smart cities. In addition, we apply both generative and causal models to produce synthetic data in order to test the accuracy of the causal models’ structure learning. What are the main findings? Probabilistic models are a feasible tool to derive reasoning from data about vulnerable groups. A causal relation between accessibility experience, demographics and disabilities is confirmed. The synthetic data generated using probabilistic models preserve the structure of real data. What is the implication of the main finding? Insights into accessibility for diverse groups will be essential in designing inclusive smart cities. Synthetic data can help overcome the insufficiency of real data while maintaining confidentiality. 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.