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DTSTAMP:20260424T141115Z
UID:629641348e5fa647774273@ist.ac.at
DTSTART:20220624T130000
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DESCRIPTION:Speaker: Caroline Uhler\nhosted by Christoph Lampert\nAbstract:
  Massive data collection holds the promise of a better understanding of co
 mplex phenomena and ultimately\, of better decisions. An exciting opportun
 ity in this regard stems from the growing availability of perturbation / i
 ntervention data (for example in genomics\, advertisement\, education\, et
 c.). In order to obtain mechanistic insights from such data\, a major chal
 lenge is the development of a framework that integrates observational and 
 interventional data and allows causal transportability\, i.e.\, predicting
  the effect of yet unseen interventions or transporting the effect of inte
 rventions observed in one context to another. I will propose an autoencode
 r framework for this problem. In particular\, I will characterize the impl
 icit bias of overparameterized autoencoders and show how this links to cau
 sal transportability and can be applied for drug repurposing in the curren
 t COVID-19 crisis.
LOCATION:Heinzel Seminar Room / Office Bldg West (I21.EG.101)\, ISTA
ORGANIZER:kharppre@ist.ac.at
SUMMARY:Caroline Uhler: Causality and Autoencoders in the Light of Drug Rep
 urposing for COVID-19
URL:https://talks-calendar.ista.ac.at/events/3805
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