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Geographic information science (GIScience) has always centered on the dual objectives of developing the basic scientific foundations needed to understand the world through the collection, analysis, and representation of spatial data, and the creation of the technological tools needed for that task. In recent years, the discipline has experienced a period of rapid tool and technology development particularly around the emergence of GeoAI. Paralleling these developments in GIScience, there has been a period of conceptual development across the social and environmental sciences in causal inference. While the pattern-process approach to spatial analysis uses the tools and concepts of GIScience to describe spatial patterns of objects and events, it remains challenging to make inferences about the causal mechanisms generating those patterns.
Within the geographic and spatial statistical literature a series of recent papers have discussed causal paradigms, introduced new spatial statistical techniques, and generally deployed methods of quantitative causal analysis to answer geographic questions. However, a robust discussion of causal inference and its connection to core concepts and questions in GIScience has yet to appear in the literature. We invite colleagues to contribute to an interactive discussion session about the connections between GIScience and causal inference. Discussion topics will include whether GIScience has a unique contribution to make to the causal literature, if the current conceptual and technical foundations of GIScience are prepared to support causal analysis, and what a GIScience curriculum designed to prepare students to undertake causal analyses would look like.
