Causal Inference for Spatial Data Analytics
Martin Tomko (University of Melbourne)
Yanan Xin (TU Delft - Traffic Systems Engineering)
Jonas Wahl (Technical University of Berlin)
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Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 24202 “Causal Inference for Spatial Data Analytics”, taking place at Schloss Dagstuhl between May 12th and 17th, 2024.
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, i.e. causal inference with spatial information offers a promising tool to address this challenge by extending causal inference methodologies to spatial domains. However, this translation is challenging due to spatial effects that might 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. To facilitate the necessary interdisciplinary exchange of ideas we convened the first Dagstuhl Seminar on Causal Inference for Spatial Data Analytics.
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