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UID:20261027T223010EDT-4003XIFBo6@132.216.98.100
DTSTAMP:20261028T023010Z
DESCRIPTION:Erin Evelyn Gabriel\, PhD\n\nAssociate Professor in Biostatisti
 cs\, University of Copenhagen\n\nWhere: Hybrid Event | 2001 McGill College
 \, Room 1140\; Zoom\n\nAbstract\n\nA causal query (estimand) will commonly
  not be identifiable from observed data without the assumption of no unmea
 sured confounders\, in which case no estimator of the query can be contriv
 ed without further assumptions or additional measured variables. However\,
  it may still be possible to derive bounds on the query in terms of the di
 stribution of observed variables. Bounds\, numeric or symbolic\, can often
  be more valuable than a statistical estimator derived under implausible a
 ssumptions. Symbolic bounds\, however\, provide a measure of uncertainty a
 nd information loss due to the lack of an identifiable estimand even in th
 e absence of data. We develop and describe a general approach for the comp
 utation of symbolic bounds and characterize a class of settings in which o
 ur method is guaranteed to provide tight valid bounds. This expands the kn
 own settings in which tight causal bounds are solutions to linear programs
  to include multicategorical variables\, cross-world nested counterfactual
 s queries\, and missing data problems. We demonstrate the usefulness of sy
 mbolic bounds and the estimates from them both conceptually for the planni
 ng of studies and in several real data settings\, including the Danish Mas
 k Study.\n\nSpeaker Bio\n\nErin is an Associate Professor of Biostatistics
  at the Section of Biostatistics in the Department of Public Health at the
  University of Copenhagen. Her research focuses on methods for causal infe
 rence and surrogate evaluation\, and design\, testing\, and estimation in 
 emulated and randomized clinical trials. Although her primary area of appl
 ication is infectious diseases\, she has recently started working more in 
 cancer and chronic illness. Erin is currently funded by the Novo Nordisk F
 oundation. Website: https://eegabriel.github.io/\n\n \n
DTSTART:20230125T203000Z
DTEND:20230125T213000Z
SUMMARY:Derivation and Usefulness of Tight Symbolic Causal Bounds 
URL:https://www.mcgill.ca/epi-biostat-occh/channels/event/derivation-and-us
 efulness-tight-symbolic-causal-bounds-344477
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