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SUMMARY:Learning and exploiting graphical structure to support valid infer
 ence for causal effects - Daniel Malinsky (Columbia University)
DTSTART:20260302T093000Z
DTEND:20260302T101500Z
UID:TALK244351@talks.cam.ac.uk
DESCRIPTION:The first part of this talk will summarize recent work on vali
 d post-selection inference for a multi-step pipeline: we want to use data 
 to learn the structure of a graphical model\, use that graph to identify s
 ome target causal effect\, and then estimate the effect along with valid c
 onfidence intervals. We achieve valid inference using a &ldquo\;resampling
  & screening&rdquo\; procedure. Then we will discuss ongoing work relevant
  to extending this pipeline when the target effect is not point-identified
  because unmeasured confounding cannot be ruled out. In that case\, we est
 imate bounds using a sensitivity model and use the independence structure 
 encoded by the graph to gain efficiency.
LOCATION:Seminar Room 1\, Newton Institute
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