This seminar is the next in SIAM PNW Distinguished Online Seminar series. The seminar can be viewed online by the link to be provided below.
A recent break-through in genomics makes it possible to perform perturbation experiments at a very large scale. The availability of such data motivates the development of a causal inference framework that is based on observational and interventional data. We first characterize the causal relationships that are identifiable from interventional data. In particular, we show that imperfect interventions, which only modify (i.e., without necessarily eliminating) the dependencies between targeted variables and their causes, provide the same causal information as perfect interventions, despite being less invasive. Second, we present the first provably consistent algorithm for learning a causal network from a mix of observational and interventional data. This requires us to develop new results in geometric combinatorics. In particular, we introduce DAG associahedra, a family of polytopes that extend the prominent graph associahedra to the directed setting. We end by discussing applications of this causal inference framework to the estimation of gene regulatory networks.
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The SIAM PNW Section https://sites.google.com/site/siampnwsection/home Seminar is a quarterly seminar presented by a distinguished speaker invited by the officers of the section. Suggestions for future speakers are welcome. Please email suggestions and requests for section involvement to one of the officers. Due to the large geographic extent of our section, the seminar series consists of online talks only, currently one per academic quarter. The talks can be attended by clicking the link provided below, which will be live 15 minutes prior to the start of the talk. No gotomeeting account is necessary. Any computer with speakers or a headphone connection will allow one to attend the talk. A microphone is required to ask questions during or after the seminar.