A Causal Inference Approach to Visitor Count at Open-Access Destinations

High visitor concentrations at open-access destinations create environmental and social challenges. Existing monitoring approaches can track visitor numbers but reveal little about factors that shape them. This paper applies causal inference theory to tourism research by showing how directed acyclic graphs can model and analyse visitor dynamics in outdoor destinations. Using two case studies from German leisure sites, it examines temporal, environmental and institutional influences on visitor count. Results show that daylight hours and weekends are the most stable predictors across both cases, while weather effects vary by destination context and activity profile. The study evaluates theory-driven hypotheses on the relative importance of temporal rhythms, environmental conditions and institutional calendars. Making assumed causal pathways explicit improves understanding the conditions associated with high visitor numbers, supports clearer research design and informs tailored management strategies. The study contributes to causal inference in tourism research and the sustainable management of open-access outdoor destinations.