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SUMMARY:Confounder selection via iterative graph expansion - Richard Guo (
 University of Cambridge)
DTSTART:20231027T143000Z
DTEND:20231027T160000Z
UID:TALK207514@talks.cam.ac.uk
CONTACT:97804
DESCRIPTION:Confounder selection\, namely choosing a set of covariates to 
 control for confounding between a treatment and an outcome\, is arguably t
 he most important step in the design of observational studies. Previous me
 thods\, such as Pearl's celebrated back-door criterion\, typically require
  pre-specifying a causal graph\, which can often be difficult in practice.
  We propose an interactive procedure for confounder selection that does no
 t require pre-specifying the graph or the set of observed variables. This 
 procedure iteratively expands the causal graph by finding what we call "pr
 imary adjustment sets" for a pair of possibly confounded variables. This c
 an be viewed as inverting a sequence of latent projections of the underlyi
 ng causal graph. Structural information in the form of primary adjustment 
 sets is elicited from the user\, bit by bit\, until either a set of covari
 ates are found to control for confounding or it can be determined that no 
 such set exists. We show that if the user correctly specifies the primary 
 adjustment sets in every step\, our procedure is both sound and complete.(
 "paper":https://arxiv.org/abs/2309.06053)
LOCATION:Centre for Mathematical Sciences\, MR12
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