Causal Effects Under Spatial Confounding and Interference (Short Paper)

Author Jing Zhang

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Jing Zhang
  • School of Geographical Sciences, University of Bristol, UK

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Jing Zhang. Causal Effects Under Spatial Confounding and Interference (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 91:1-91:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)


Spatial causal inference is an emerging field of research with wide ranging areas of applications. As a key methodological challenge, spatial confounding and spatial interference can compromise the performance of standard statistical inference methods. In the current literature, there is a lack of appreciation of the connections between spatial confounding and interference. This could potentially lead to overspecialized silos of research. Therefore, we need further research to bridge such gaps theoretically, and to find creative solutions for complex spatial causal inference problems. This short paper offers a brief demonstration: It discusses the connections between spatial confounding and interference. An illustrative simulation study shows how commonly used approaches compare across four test scenarios. The simulation study is discussed with an emphasis on the promising performance of counterfactual prediction based inference methods.

Subject Classification

ACM Subject Classification
  • Applied computing → Law, social and behavioral sciences
  • Spatial causal inference
  • confounding
  • interference
  • counterfactual


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  1. Carlos Cinelli, Daniel Kumor, Bryant Chen, Judea Pearl, and Elias Bareinboim. Sensitivity analysis of linear structural causal models. In International conference on machine learning, pages 1252-1261. PMLR, 2019. Google Scholar
  2. Melanie L Davis, Brian Neelon, Paul J Nietert, Kelly J Hunt, Lane F Burgette, Andrew B Lawson, and Leonard E Egede. Addressing geographic confounding through spatial propensity scores: a study of racial disparities in diabetes. Statistical Methods in Medical Research, 28(3):734-748, 2019. Google Scholar
  3. W Dana Flanders, Matthew J Strickland, and Mitchel Klein. A new method for partial correction of residual confounding in time-series and other observational studies. American journal of epidemiology, 185(10):941-949, 2017. Google Scholar
  4. Laura Forastiere, Edoardo M Airoldi, and Fabrizia Mealli. Identification and estimation of treatment and interference effects in observational studies on networks. Journal of the American Statistical Association, 116(534):901-918, 2021. Google Scholar
  5. Nathan Kallus, Xiaojie Mao, and Angela Zhou. Interval estimation of individual-level causal effects under unobserved confounding. In The 22nd international conference on artificial intelligence and statistics, pages 2281-2290. PMLR, 2019. Google Scholar
  6. Charles F Manski. Identification of treatment response with social interactions. The Econometrics Journal, 16(1):S1-S23, 2013. Google Scholar
  7. Georgia Papadogeorgou, Fabrizia Mealli, and Corwin M Zigler. Causal inference with interfering units for cluster and population level treatment allocation programs. Biometrics, 75(3):778-787, 2019. Google Scholar
  8. Brian J Reich, Shu Yang, Yawen Guan, Andrew B Giffin, Matthew J Miller, and Ana Rappold. A review of spatial causal inference methods for environmental and epidemiological applications. International Statistical Review, 89(3):605-634, 2021. Google Scholar
  9. Donald B Rubin. Formal mode of statistical inference for causal effects. Journal of statistical planning and inference, 25(3):279-292, 1990. Google Scholar
  10. Eric J Tchetgen Tchetgen and Tyler J VanderWeele. On causal inference in the presence of interference. Statistical methods in medical research, 21(1):55-75, 2012. Google Scholar
  11. Tyler J VanderWeele, Eric J Tchetgen Tchetgen, and M Elizabeth Halloran. Interference and sensitivity analysis. Statistical science: a review journal of the Institute of Mathematical Statistics, 29(4):687, 2014. Google Scholar
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