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SUMMARY:Spatial causal inference in the presence of unmeasured confounding
  and interference - Georgia Padadogeorgou\, University of Florida
DTSTART:20240426T130000Z
DTEND:20240426T140000Z
UID:TALK213460@talks.cam.ac.uk
CONTACT:Dr Sergio Bacallado
DESCRIPTION:In this talk\, I aim to bridge the divide between causal infer
 ence and spatial statistics\, by presenting novel insights for causal infe
 rence in spatial data analysis and establishing how tools from spatial sta
 tistics can be used to draw causal inferences. I will introduce spatial ca
 usal graphs to highlight that spatial confounding and interference can be 
 entangled\, in that investigating the presence of one can lead to wrongful
  conclusions in the presence of the other. Moreover\, I will illustrate th
 at spatial dependence in the exposure variable can render standard analyse
 s invalid\, which can lead to erroneous conclusions. To remedy these issue
 s\, we propose a Bayesian parametric approach based on tools commonly-used
  in spatial statistics. This approach simultaneously accounts for interfer
 ence and mitigates bias resulting from local and neighbourhood unmeasured 
 spatial confounding. From a Bayesian perspective\, we show that incorporat
 ing an exposure model is necessary\, and we theoretically prove that all m
 odel parameters are identifiable\, even in the presence of unmeasured conf
 ounding. We study the impact of sulfur dioxide emissions from power plants
  on cardiovascular mortality.
LOCATION:MR12\, Centre for Mathematical Sciences
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