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SUMMARY:A machine learning approach for causal structure estimation in hig
 h dimensions - Sach Mukherjee (MRC Biostatistics Unit)
DTSTART:20220603T130000Z
DTEND:20220603T140000Z
UID:TALK173324@talks.cam.ac.uk
CONTACT:Qingyuan Zhao
DESCRIPTION:Causal structure learning refers to the task of estimating gra
 phical structures encoding causal relationships between variables. This re
 mains challenging\, especially under conditions of high dimensionality\, l
 atent variables and noisy\, finite data\, as seen in many real world appli
 cations. I will discuss our recent efforts to reframe specific aspects of 
 causal structure learning from a machine learning perspective. The approac
 hes I will discuss differ from classical structure learning tools in that 
 rather than trying to establish a model of the data-generating process\, t
 hey focus on minimizing a certain expected loss defined with respect to th
 e causal structure of interest. The work is motivated by applications in h
 igh-dimensional molecular biology\, and I will show empirical examples in 
 which model-based predictions can be tested at large scale against experim
 ental results.\n
LOCATION:https://maths-cam-ac-uk.zoom.us/j/93998865836?pwd=VzVzN1VFQ0xjS3V
 DdlY0enBVckY5dz09
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