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UID:20261026T200512EDT-4719sikm7n@132.216.98.100
DTSTAMP:20261027T000512Z
DESCRIPTION:Linbo Wang\, PhD\n\nAssistant Professor\n	Department of Statisti
 cal Sciences | University of Toronto\n\nWHEN: Wednesday\, November 15\, 20
 23\, from 3:30 to 4:30 p.m.\n\nWHERE: Hybrid | 2001 McGill College\, Rm 11
 40 | Zoom &\n\nNote: Dr. Wang will present from Toronto\n\nAbstract\n\nIn 
 many observational studies\, researchers are often interested in studying 
 the effects of multiple exposures on a single outcome. Standard approaches
  for high-dimensional data such as the lasso assume the associations betwe
 en the exposures and the outcome are sparse. These methods\, however\, do 
 not estimate the causal effects in the presence of unmeasured confounding.
  In this paper\, we consider an alternative approach that assumes the caus
 al effects in view are sparse. We show that with sparse causation\, the ca
 usal effects are identifiable even with unmeasured confounding. At the cor
 e of our proposal is a novel device\, called the synthetic instrument\, th
 at in contrast to standard instrumental variables\, can be constructed usi
 ng the observed exposures directly. We show that under linear structural e
 quation models\, the problem of causal effect estimation can be formulated
  as an ℓ0-penalization problem\, and hence can be solved efficiently using
  off-the-shelf software. Simulations show that our approach outperforms st
 ate-of-art methods in both low-dimensional and high-dimensional settings. 
 We further illustrate our method using a mouse obesity dataset.\n\nSpeaker
  Bio\n\nhttps://sites.google.com/site/linbowangpku/home\n
DTSTART:20231115T203000Z
DTEND:20231115T213000Z
SUMMARY:The synthetic instrument: From sparse association to  sparse causat
 ion
URL:https://www.mcgill.ca/epi-biostat-occh/channels/event/synthetic-instrum
 ent-sparse-association-sparse-causation-352629
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