STCausal 2024


1st ACM SIGSPATIAL International Workshop on Spatiotemporal Causal Analysis (STCausal Workshop 2024)

October 29th, 2024 in Atlanta, GA, USA
at the 32nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2024)


Causal analysis, including causal discovery, causal inference and causal representation learning, is fundamental to understanding the behaviors of a natural and societal system, and therefore making proper decisions. Most traditional causal analysis has been built for I.I.D data. However, performing causal analysis in many scientific disciplines such as Earth science, epidemiology, transportation, urban planning, and economics, requires working on temporal, spatial, or spatiotemporal data. According to literature, there is still a lack of research in causal analysis of these types of datasets.

Our workshop will be an engaging mini-conference designed to explore the dynamic and intricate relationships between spatial and temporal data in understanding causality. This workshop will bring together experts, researchers, and practitioners from diverse fields to discuss cutting-edge methodologies, share innovative research, and foster collaborative discussions on the challenges and opportunities in spatiotemporal causal analysis.

Topics of interests include, but are not limited to:

  • Causal discovery from temporal, spatial and spatiotemporal data.
  • Causal inference from temporal, spatial and spatiotemporal data.
  • Causal representation learning from temporal, spatial and spatiotemporal data.
  • Deep/machine learning for causality.
  • Causality for deep/machine learning.
  • Spatiotemporal causal analysis applications in scientific disciplines (earth science, epidemiology, transportation, urban planning, etc).
  • Spatiotemporal causal analysis applications in business and public sectors.
  • Open-source tools, datasets and demonstrations for temporal, spatial and spatiotemporal causal analysis.
  • Benchmarking across multiple spatiotemporal causal analysis algorithms & tools and/or across multiple spatiotemporal data.

Important Dates

  • Paper Submission: September 1st, 2024
  • Decision Notification: September 29th, 2024
  • Camera-Ready Due Date: October 4th, 2024
  • Workshop Date: October 29th, 2024

All submissions are due at 11:59 pm EDT.

Paper Submission

The workshop seeks high-quality full (8-10 pages) and short (4 pages) papers that have not been published in other academic outlets and are not concurrently under peer review.

Manuscripts should be submitted in PDF format and formatted using the ACM camera-ready templates available at SIGSPATIAL uses the Conference Proceedings Primary Article template with two-column format. Alterations to the template, especially to gain more space, will be grounds for administrative rejection without further technical review. The author list is considered to be final after the submission deadline and no changes to the author list are allowed for accepted papers.

Submission link:

Accepted papers will be published in the ACM Digital Library. At least one author of each accepted paper is required to attend the workshop and present the paper.

Workshop Chairs

  • Jianwu Wang, Professor of Data Science, Department of Information Systems, University of Maryland, Baltimore County, US
  • Sahara Ali, Assistant Professor, Department of Information Science, University of North Texas, US
  • Yanan Xin, ETH Zürich, CH and incoming Assistant Professor, Department of Transport and Planning, Delft University of Technology,  NL

Program Committee (To be updated)

  • Kevin Credit (Maynooth University, IE)
  • Simon Dirmeier (Swiss Data Science Center – Zürich, CH)
  • Andreas Gerhardus (DLR – Jena, DE)
  • Totte Harinen (Airbnb – San Francisco, US)
  • Dominik Janzing (Amazon Web Services – Tübingen, DE)
  • Urmi Ninad (TU Berlin, DE)
  • Markus Reichstein (MPI für Biogeochemie – Jena, DE)
  • Katerina Schindlerova (Universität Wien, AT)
  • Martin Tomko (University of Melbourne – Carlton, AU)
  • Jonas Wahl (TU Berlin, DE)
  • Levi John Wolf (University of Bristol, GB)
  • Shu Yang (North Carolina State University – Raleigh, US)
  • Andrew Zammit Mangion (University of Wollongong, AU)