[Statlist] Tuesday, June 13, 2017 with Caroline Uhler (Massachusetts Institute of Technology IDSS)

[Maurer Letizia]

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ETH and University of Zurich

Organisers:

Proff. P. Bühlmann - L. Held - T. Hothorn - M. Maathuis -
N. Meinshausen - S. van de Geer - M. Wolf
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We are glad to announce the following talk:

Tuesday, June 13, 2017 at 11.15h  ETH Zurich HG G 19.241
with Caroline Uhler (Massachusetts Institute of Technology IDSS)
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Title:

Permutation-based causal inference algorithms with interventions

Abstract:

A recent break-through in genomics makes it possible to perform perturbation experiments at a very large scale. In order to learn gene regulatory networks from the resulting data, efficient and reliable causal inference algorithms are needed that can make use of both, observational and interventional data. I will present an algorithm of this type and prove that it is consistent under the faithfulness assumption. This algorithm is based on a greedy permutation search and it is a hybrid approach that uses conditional independence relations in a score-based method.  Hence, this algorithm is non-parametric, which makes it useful for analyzing inherently non-Gaussian gene expression data. We will end by analyzing its performance on simulated data, protein signaling data, and single-cell gene expression data.


This abstract is also to be found under the following link: http://stat.ethz.ch/events/research_seminar

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