License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.COSIT.2022.23
URN: urn:nbn:de:0030-drops-169087
URL: https://drops.dagstuhl.de/opus/volltexte/2022/16908/
Go to the corresponding LIPIcs Volume Portal


Akbari, Kamal ; Tomko, Martin

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

pdf-format:
LIPIcs-COSIT-2022-23.pdf (0.7 MB)


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.

BibTeX - Entry

@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.dagstuhl.de/opus/volltexte/2022/16908},
  URN =		{urn:nbn:de:0030-drops-169087},
  doi =		{10.4230/LIPIcs.COSIT.2022.23},
  annote =	{Keywords: Framework, Spatial, Spatiotemporal, Matching, Causal Inference}
}

Keywords: Framework, Spatial, Spatiotemporal, Matching, Causal Inference
Collection: 15th International Conference on Spatial Information Theory (COSIT 2022)
Issue Date: 2022
Date of publication: 22.08.2022


DROPS-Home | Fulltext Search | Imprint | Privacy Published by LZI