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DTSTART:20070311T020000
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DTSTAMP:20250401T092559
DTSTART;TZID=America/Detroit:20250411T100000
DTEND;TZID=America/Detroit:20250411T110000
SUMMARY:Workshop / Seminar:Statistics Department Michael Woodroofe Lecture Series: Nicolai Meinshausen\, Professor\, Department of Statistics\, ETH Zurich.
DESCRIPTION:Abstract: Distributional regression aims to estimate the full conditional distribution of a target variable\, given covariates. Traditional approaches include linear or tree-based quantile regression. Modern computational-intensive approaches include diffusion models and flow matching. It is shown how a simple extension of the absolute error loss of standard regression yields a light-weight generative model that can easily be extended to very high-dimensional targets. It also can be used in an instrumental variable setting to estimate the full conditional distribution under interventions on the treatment variable. Results are very robust to the chosen model size and there are advantages of a distributional fit even if we only care about conditional mean estimation\, whether observational or interventional. The advantages include robustness properties under mild extrapolation \nin a regression setting and better identifiability results for causal effect estimation. \n\nhttps://people.math.ethz.ch/~nicolai/
UID:132387-21870854@events.umich.edu
URL:https://events.umich.edu/event/132387
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:seminar
LOCATION:West Hall - 340
CONTACT:
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