The ability to identify causal relationships in spatial data is increasingly important for designing effective policy interventions in environmental science, epidemiology, urban planning, and traffic management. Current spatial data analytic methods rely mainly on descriptive and predictive methods that lack explicit causal models. Spatial causal inference offers a promising solution to address this challenge by extending causal inference methodologies to spatial domains. However, this translation is challenging due to spatial effects that violate fundamental assumptions of causal inference. Spatial causal inference is therefore still in its infancy, and there is a pressing need to accelerate its theoretical development and support its adoption with a well-grounded methodological toolset. This requires an interdisciplinary exchange of ideas, as researchers in different fields, such as environmental sciences, spatial statistics, theoretical GIScience, and machine learning, are making significant but disparate efforts in the foundations of spatial causal inference. To address these challenges, we convene the first Dagstuhl Seminar on Causal Inference for Spatial Data Analytics.