A Causal Approach to Investigating Accessibility Experiences of Persons with Disabilities

This paper investigates causal dependencies in the data on accessibility barriers in federal sector organizations encountered by persons with disabilities in Canada. By integrating multivariate statistical and probabilistic causal modeling techniques such as Structural equation modeling and Bayesian networks, our approach analyzes the links between the nature of barriers and type of disability. The proposed framework aims to provide reasoning through parameter analysis and causal inference to understand the prevalence of barriers and challenges faced by persons with disabilities. This contributes to identifying crucial areas to facilitate accessibility regulation and practices.