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Short Paper
DOI: 10.4230/LIPIcs.COSIT.2022.23
Spatial and Spatiotemporal Matching Framework for Causal Inference (Short Paper)

Authors: Kamal Akbari and Martin Tomko

Published in: LIPIcs, Volume 240, 15th International Conference on Spatial Information Theory (COSIT 2022)

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.
Cite as

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)

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@InProceedings{akbari_et_al:LIPIcs.COSIT.2022.23,
  author =	{Akbari, Kamal and Tomko, Martin},
  title =	{{Spatial and Spatiotemporal Matching Framework for Causal Inference}},
  booktitle =	{15th International Conference on Spatial Information Theory (COSIT 2022)},
  pages =	{23:1--23:7},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-257-0},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{240},
  editor =	{Ishikawa, Toru and Fabrikant, Sara Irina and Winter, Stephan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.COSIT.2022.23},
  URN =		{urn:nbn:de:0030-drops-169087},
  doi =		{10.4230/LIPIcs.COSIT.2022.23},
  annote =	{Keywords: Framework, Spatial, Spatiotemporal, Matching, Causal Inference}
}
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