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SUMMARY:Marcel Wienöbst (University of Lübeck)
DTSTART:20241106T090000Z
DTEND:20241106T094500Z
DTSTAMP:20260422T155325Z
UID:gbgstats/70
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/gbgstats/70/
 ">Linear-Time Algorithms for Front-Door Adjustment in Causal Graphs</a>\nb
 y Marcel Wienöbst (University of Lübeck) as part of Gothenburg statistic
 s seminar\n\nLecture held in MVL14.\n\nAbstract\nCausal effect estimation 
 from observational data is a fundamental task in\nempirical sciences. It b
 ecomes particularly challenging when unobserved\nconfounders are involved 
 in a system. Front-door adjustment constitutes a\nclassic method that allo
 ws identifying the causal effect even in the presence of\nlatent confoundi
 ng by using observed mediators. This talk presents a recent\nalgorithmic r
 esult in this area\, namely a linear-time algorithm for finding a\nfront-d
 oor adjustment set in a given causal graph. Its run-time is\nasymptoticall
 y optimal and improves on the previous state-of-the-art for this\ntask by 
 a factor that grows cubically in the number of variables. Beyond this\nres
 ult\, the presentation explores fundamental algorithmic tools and techniqu
 es\nuseful for broader applications in causal inference.\n
LOCATION:https://researchseminars.org/talk/gbgstats/70/
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