Estimating Direct and Indirect Causal Effects of Spatiotemporal Interventions in Presence of Spatial Interference

dc.contributor.authorAli, Sahara
dc.contributor.authorFaruque, Omar
dc.contributor.authorWang, Jianwu
dc.date.accessioned2024-06-11T15:08:33Z
dc.date.available2024-06-11T15:08:33Z
dc.date.issued2024-05-13
dc.description.abstractSpatial interference (SI) occurs when the treatment at one location affects the outcomes at other locations. Accounting for spatial interference in spatiotemporal settings poses further challenges as interference violates the stable unit treatment value assumption, making it infeasible for standard causal inference methods to quantify the effects of time-varying treatment at spatially varying outcomes. In this paper, we first formalize the concept of spatial interference in case of time-varying treatment assignments by extending the potential outcome framework under the assumption of no unmeasured confounding. We then propose our deep learning based potential outcome model for spatiotemporal causal inference. We utilize latent factor modeling to reduce the bias due to time-varying confounding while leveraging the power of U-Net architecture to capture global and local spatial interference in data over time. Our causal estimators are an extension of average treatment effect (ATE) for estimating direct (DATE) and indirect effects (IATE) of spatial interference on treated and untreated data. Being the first of its kind deep learning based spatiotemporal causal inference technique, our approach shows advantages over several baseline methods based on the experiment results on two synthetic datasets, with and without spatial interference. Our results on real-world climate dataset also align with domain knowledge, further demonstrating the effectiveness of our proposed method.
dc.description.urihttp://arxiv.org/abs/2405.08174
dc.format.extent16 pages
dc.genrejournal articles
dc.genrepreprints
dc.identifierdoi:10.13016/m2yq10-ehtm
dc.identifier.urihttps://doi.org/10.48550/arXiv.2405.08174
dc.identifier.urihttp://hdl.handle.net/11603/34601
dc.language.isoen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.rightsCC BY 4.0 DEED Attribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectComputer Science - Artificial Intelligence
dc.subjectComputer Science - Machine Learning
dc.titleEstimating Direct and Indirect Causal Effects of Spatiotemporal Interventions in Presence of Spatial Interference
dc.typeText
dcterms.creatorhttps://orcid.org/0000-0002-9933-1170

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