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UID:20250624T123937EDT-0870GVEEbG@132.216.98.100
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DESCRIPTION:Title: A Spectral Adjustment for Spatial Confounding.\n\nAbstra
 ct: Brian is the Gertrude M Cox Distinguished Professor of Statistics at N
 orth Carolina State University. He completed his PhD in Biostatistics in 2
 005 under the direction of Jim Hodges of the University of Minnesota. Afte
 r graduation\, he joined NC State first as a post-doc with Montse Fuentes 
 and then as a member of the statistics faculty in 2008. His research inter
 ests include spatial statistics\, extreme value analysis\, variable select
 ion and dimension reduction. In addition to these methodological interests
 \, Brian applies these methods to environmental areas such as ecology\, ai
 r pollution\, and climate change\, as well as data from the physical and m
 aterials sciences. Please visit: https://bjreich.wordpress.ncsu.edu/\n\n\n
 Adjusting for an unmeasured confounder is generally an intractable problem
 \, but in the spatial setting it may be possible under certain conditions.
  In this paper\, we derive necessary conditions on the coherence between t
 he treatment variable of interest and the unmeasured confounder that ensur
 e the causal effect of the treatment is estimable. We specify our model an
 d assumptions in the spectral domain to allow for different degrees of con
 founding at different spatial resolutions. The key assumption that ensures
  identifiability is that confounding present at global scales dissipates a
 t local scales. We show that this assumption in the spectral domain is equ
 ivalent to adjusting for global-scale confounding in the spatial domain by
  adding a spatially smoothed version of the treatment variable to the mean
  of the response variable. Within this general framework\, we propose a se
 quence of confounder adjustment methods that range from parametric adjustm
 ents based on the Matern coherence function to more robust semi-parametric
  methods that use smoothing splines. These ideas are applied to areal and 
 geostatistical data for both simulated and real datasets.\n\nEpidemiology\
 , Biostatistics\, & Occupational Health\n	Via Zoom: https://mcgill.zoom.us/
 j/88625559031?pwd=NGxGSkU3bG9rZmRXTG82dFBNeXc3Zz09\n
DTSTART:20210317T193000Z
DTEND:20210317T203000Z
SUMMARY:Brian J. Reich (North Carolina State University)
URL:/mathstat/channels/event/brian-j-reich-north-carol
 ina-state-university-329588
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