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SUMMARY:Confidence in Causal Discovery with Linear Causal Models
DTSTART;VALUE=DATE-TIME:20220803T120000Z
DTEND;VALUE=DATE-TIME:20220803T122000Z
DTSTAMP;VALUE=DATE-TIME:20240618T032706Z
UID:indico-contribution-1227@conference2.aau.at
DESCRIPTION:Inferring causal relations of a system is a fundamental proble
m of statistics. A widely studied approach employs structural causal model
s that model noisy functional relations among a set of interacting variabl
es. The underlying causal structure is naturally represented by a directed
graph whose edges indicate direct causal dependencies. Under the assumpti
on of linear relations with homoscedastic Gaussian errors this causal grap
h and\, thus also\, causal effects are identifiable from mere observationa
l data. Over the past decade\, two main lines of research evolved\, learni
ng the causal graph as well as estimating causal effects when the graph is
known. However\, a two-step method\, that first learns a graph and then t
reats the graph as known yields confidence intervals that are overly optim
istic and can drastically fail to account for the uncertain causal structu
re. In this talk\, I will address this issue and present a framework based
on test inversion that allows us to give confidence regions for total cau
sal effects that capture both sources of uncertainty: causal structure and
numerical size of nonzero effects.\n\nhttps://conference2.aau.at/event/13
1/contributions/1227/
LOCATION:Universität Klagenfurt HS 4
URL:https://conference2.aau.at/event/131/contributions/1227/
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