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SUMMARY:Stochastic Causal Programming for Bounding Treatment Effects - Ric
 ardo Silva (UCL)
DTSTART:20230224T140000Z
DTEND:20230224T150000Z
UID:TALK194905@talks.cam.ac.uk
CONTACT:Qingyuan Zhao
DESCRIPTION:Causal effect estimation is important for many tasks in the na
 tural and social sciences. We design algorithms for the continuous partial
  identification problem: bounding the effects of multivariate\, continuous
  treatments when unmeasured confounding makes identification impossible. S
 pecifically\, we cast causal effects as objective functions within a const
 rained optimization problem\, and minimize/maximize these functions to obt
 ain bounds. We combine flexible learning algorithms with Monte Carlo metho
 ds to implement a family of solutions under the name of stochastic causal 
 programming. In particular\, we show how the generic framework can be effi
 ciently formulated in settings where auxiliary variables are clustered int
 o pre-treatment and post-treatment sets\, where no fine-grained causal gra
 ph can be formulated. Contrasted to other generic approaches\, this highly
  simplifies the problem and has advantages concerning how to encode struct
 ural knowledge without explicitly constructing latent hidden common causes
 .\n \nJoint work with Kirtan Padh\, Jakob Zeitler\, David Watson\, Matt Ku
 sner and Niki Kilbertus.
LOCATION:MR12\, Centre for Mathematical Sciences
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