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SUMMARY:Assessing the Sensitivity to Unmeasured Confounding in Observation
 al Studies
DTSTART:20210824T160000
DTEND:20210824T180000
DTSTAMP:20260511T044736Z
UID:92a580d7a4d1fd3aacab5846542db95339b51fa5eb74f78a5755b702
CATEGORIES:Conferences - Seminars
DESCRIPTION:Sina Akbari\nEDIC candidacy exam\nexam president: Prof. Ruedig
 er Urbanke\nthesis advisor: Prof. Negar Kiyavash\nco-examiner: Prof. Patri
 ck Thiran\n\nAbstract\nCausal inference with observational data is highly 
 motivated for estimating associations without unknown mechanisms. However\
 , inference with observational data usually relies on assumptions that are
  not testable from data such as unconfoundedness.  It is therefore crucia
 l to assess the sensitivity of the inference to such assumptions. In this 
 report\, we discuss `sensitivity analysis' as a tool to evaluate the robus
 tness of causal effect estimates to unmeasured confounding. Sensitivity an
 alysis allows for details that can be given and discussed about the degree
  of confounding that would invalidate the causal conclusions. The expert t
 hen can make judgments about the plausibility of such strong confounders i
 n the observational study\, within a formal and precise framework.\n\nBack
 ground papers\n\n	Making Sense of Sensitivity: Extending Omitted Variable 
 Bias (Carlos Cinelli and Chad Hazlett)\n	Sense and Sensitivity Analysis: S
 imple Post-Hoc Analysis of Bias Due to Unobserved Confounding (Victor Veit
 ch and Anisha Zaveri)\n	Flexible Sensitivity Analysis for Observational St
 udies Without Observable Implications (Alexander M. Franks\, Alexander D
 ’Amour\, and Avi Feller)\n
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STATUS:CONFIRMED
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