Spatio-Causal Modeling and Applications @ SIGSPATIAL'26

Schedule

The tutorial will take place at SIGSPATIAL'26, TBA, in TBA.

Tutorial

In this 90-minute long tutorial, we will explore the field of Causality, the state of the art, Spatio-Causality, Spatio-Causal Algorithms and its applications over four different sub-domain tasks, a hands-on spatio-causal modeling experience using CausalBench, and then conclude with a discussion of the tutorial topics.

Read the tutorial paper here.

Tutorial Abstract

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Tutorial Material

We provide several tutorial materials below for your convenience and reference. These materials may be updated in the future to provide the best CausalBench experience.

Slides TBA

You may access the slides here.

Quickstart file (Google Colab)

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Presenters

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Program Outline

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Covered Topics

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Documentation

You can access CausalBench documentation at docs.causalbench.org, which is the acting knowledge base for CausalBench.

References

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Contact

For any questions regarding tutorial, please contact us at support@causalbench.org or akapkic@asu.edu.

Acknowledgments

We thank all the contributors and the community for their continuous support and feedback in making CausalBench a reliable and valuable resource for causal learning research. This research is funded by NSF Grant 2311716, "CausalBench: A Cyberinfrastructure for Causal-Learning Benchmarking for Efficacy, Reproducibility, and Scientific Collaboration", and NSF Grants #2230748, "PIRE: Building Decarbonization via AI-empowered District Heat Pump Systems", #2412115, "PIPP Phase II: Analysis and Prediction of Pandemic Expansion (APPEX)" and USACE #GR40695, "Designing nature to enhance resilience of built infrastructure in western US landscapes".