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Activity Number: 444 - Highlights from the Journal STAT
Type: Topic Contributed
Date/Time: Thursday, August 6, 2020 : 10:00 AM to 11:50 AM
Sponsor: SSC (Statistical Society of Canada)
Abstract #313663
Title: From Causal Inference to Gene Regulation
Author(s): Caroline Uhler*
Companies: Massachusetts Institute of Technology
Keywords: Causality; Gene regulation; Graphical models; Permutations

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 by performing a greedy search on the space of permutations. We end by discussing applications of this causal inference framework to the estimation of gene regulatory networks.

Authors who are presenting talks have a * after their name.

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