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SUMMARY:Autoencoders and Causality in the Light of Drug Repurposing for CO
 VID-19 - Caroline Uhler (Massachusetts Institute of Technology)
DTSTART:20211123T150000Z
DTEND:20211123T160000Z
UID:TALK164863@talks.cam.ac.uk
DESCRIPTION:Massive data collection holds the promise of a better understa
 nding of complex phenomena and ultimately\, of better decisions. An exciti
 ng opportunity in this regard stems from the growing availability of pertu
 rbation / intervention data (for example in genomics\, advertisement\, edu
 cation\, etc.). In order to obtain mechanistic insights from such data\, a
  major challenge is the development of a framework that integrates observa
 tional and interventional data and allows causal transportability\, i.e.\,
  predicting the effect of yet unseen interventions or transporting the eff
 ect of interventions observed in one context to another. I will propose an
  autoencoder framework for this problem. In particular\, I will characteri
 ze the implicit bias of overparameterized autoencoders and show how this l
 inks to causal transportability and can be applied for drug repurposing in
  the current COVID-19 crisis.
LOCATION:Seminar Room 1\, Newton Institute
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