B1281
Title: Permutation-based causal inference algorithms with interventions
Authors: Yuhao Wang - MIT (United States)
Liam Solus - KTH Royal Institute of Technology (Sweden)
Karren Yang - MIT (United States)
Caroline Uhler - Massachusetts Institute of Technology (United States) [presenting]
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. We will present the first provably consistent such algorithm. 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.
