The challenge of spatial confounding is central to scientific studies involving geographical data. This paper introduces SpaCE, a comprehensive toolkit designed to provide benchmark datasets and tools for causal inference research specifically tailored to these issues. Highlights include:

• Development of realistic benchmark datasets featuring training data, true c Counter Factuals, and multiple metrics like smoothness and confounding scores.
• The toolkit includes tools for an end-to-end pipeline that simplifies data loading, experimental setup, and evaluation of models on geographic datasets.
• Extensive testing across various domains demonstrates the robustness and versatility of SpaCE in providing effective benchmarking solutions.

Key Takeaways:

• SpaCE tackles the underexplored area of spatial confounding with novel datasets and methodological benchmarks.
• This toolkit enables researchers to rigorously test and improve causal inference methods, potentially leading to more accurate insights in fields such as epidemiology and environmental science.

The introduction of SpaCE may catalyze a new wave of innovations in handling complex, geographically-linked data. Further exploration could extend these methodologies to more varied environmental and societal datasets, enriching the landscape of causal inference research.