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Spatial and Spatiotemporal Matching Framework for Causal Inference (Short Paper)

Authors Kamal Akbari , Martin Tomko

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Kamal Akbari
  • Faculty of Engineering and Information Technology, The University of Melbourne, Australia
Martin Tomko
  • Faculty of Engineering and Information Technology, The University of Melbourne, Australia

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Kamal Akbari and Martin Tomko. Spatial and Spatiotemporal Matching Framework for Causal Inference (Short Paper). In 15th International Conference on Spatial Information Theory (COSIT 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 240, pp. 23:1-23:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022) https://doi.org/10.4230/LIPIcs.COSIT.2022.23

Abstract

Matching is a procedure aimed at reducing the impact of observational data bias in causal analysis. Designing matching methods for spatial data reflecting static spatial or dynamic spatio-temporal processes is complex because of the effects of spatial dependence and spatial heterogeneity. Both may be compounded with temporal lag in the dependency effects on the study units. Current matching techniques based on similarity indexes and pairing strategies need to be extended with optimal spatial matching procedures. Here, we propose a decision framework to support analysts through the choice of existing matching methods and anticipate the development of specialized matching methods for spatial data. This framework thus enables to identify knowledge gaps.

Subject Classification

ACM Subject Classification
  • Mathematics of computing → Causal networks
  • Information systems → Spatial-temporal systems
  • Information systems → Data analytics
Keywords
  • Framework
  • Spatial
  • Spatiotemporal
  • Matching
  • Causal Inference

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References

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