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SUMMARY:Half-Trek Criterion for Identifiability of Latent Variable Models
DTSTART;VALUE=DATE-TIME:20220803T113000Z
DTEND;VALUE=DATE-TIME:20220803T115000Z
DTSTAMP;VALUE=DATE-TIME:20240523T205042Z
UID:indico-contribution-1219@conference2.aau.at
DESCRIPTION:Linear structural equation models relate random variables of i
nterest via a linear equation system that features stochastic noise. Each
model corresponds to a directed graph whose edges represent the non-zero c
oefficients in the equation system. Prior research has developed a variety
of methods to decide parameter identifiability in models with latent vari
ables. Identifiability holds if the coefficients associated with the edges
of the graph can be uniquely recovered from the covariance matrix they de
fine. The methods usually operate in a latent projection framework where t
he confounding effects of the latent variables are represented by correlat
ion among noise terms and this approach is effective when latent confoundi
ng is sparse. In this talk I will present a new combinatorial criterion fo
r parameter identifiability that operates on the original unprojected late
nt variable model and is able to certify identifiability in settings\, whe
re some latent variables may also have dense effects on many or even all o
f the observables.\n\nhttps://conference2.aau.at/event/131/contributions/1
219/
LOCATION:Universität Klagenfurt HS 4
URL:https://conference2.aau.at/event/131/contributions/1219/
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