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SUMMARY:Causal de Finetti: On the Identification of Invariant Causal Struc
 ture in Exchangeable Data - Siyuan Guo (University of Cambridge)
DTSTART:20220704T123000Z
DTEND:20220704T140000Z
UID:TALK175457@talks.cam.ac.uk
CONTACT:Qingyuan Zhao
DESCRIPTION:Learning invariant causal structure often relies on conditiona
 l independence testing and assumption of independent and identically distr
 ibuted data. Recent work has explored inferring invariant causal structure
  using data coming from different environments. These approaches are based
  on independent causal mechanism (ICM) principle which postulates that the
  cause mechanism is independent of the effect given cause mechanism. Despi
 te its wide application in machine learning and causal inference\, there l
 acks a statistical formalization of what independent mechanism means. Here
  we present Causal de Finetti which offers a first statistical formalizati
 on of ICM principle.
LOCATION:MR12\,  Centre for Mathematical Sciences\, Wilberforce Road\, Cam
 bridge
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