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X-WR-CALNAME:Causal Inference with Unstructured Data - Yixin Wang\, Assista
 nt Professor\, University of Michigan
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TZUNTIL:20261101T090000Z
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CREATED:20250221T211325Z
DESCRIPTION:Zoom Link Abstract: Causal inference traditionally involves ana
 lyzing tabular data where variables like treatment\, outcome\, covariates\
 , and colliders are manually labeled by humans. However\, many complex cau
 sal inference problems rely on unstructured data sources such as images\, 
 text and videos that depict overall situations. These causal problems requ
 ire a crucial first step - extracting the high-level latent causal factors
  from the low-level unstructured data inputs\, a task known as 'causal rep
 resentation learning.' In this talk\, we explore how to identify latent ca
 usal factors from…
DTSTART;TZID=America/Vancouver:20250304T110000
DTEND;TZID=America/Vancouver:20250304T120000
LAST-MODIFIED:20250221T212711Z
LOCATION:UBC Vancouver Campus\, ICCS X836 / Zoom
SUMMARY:Causal Inference with Unstructured Data - Yixin Wang\, Assistant Pr
 ofessor\, University of Michigan
TRANSP:OPAQUE
URL:https://caida.ubc.ca/index.php/event/causal-inference-unstructured-data
 -yixin-wang-assistant-professor-university-michigan
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