Direct Estimation of Differences in Causal Graphs
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7634-direct-estimation-of-differences-in-causal-graphs.pdf
Description
Published version
Size
726.47 KB
Format
Adobe PDF
Checksum (MD5)
8ee95d78958a34bd192e269e91884e45
Author(s)
Wang, Yuhao
Squires, Chandler
Belyaeva, Anastasiya
Uhler, Caroline
Date Issued
2018
Citation
Wang, Yuhao, Squires, Chandler, Belyaeva, Anastasiya and Uhler, Caroline. 2018. "Direct Estimation of Differences in Causal Graphs."
Version
Final published version
Abstract
© 2018 Curran Associates Inc..All rights reserved. We consider the problem of estimating the differences between two causal directed acyclic graph (DAG) models with a shared topological order given i.i.d. samples from each model. This is of interest for example in genomics, where changes in the structure or edge weights of the underlying causal graphs reflect alterations in the gene regulatory networks. We here provide the first provably consistent method for directly estimating the differences in a pair of causal DAGs without separately learning two possibly large and dense DAG models and computing their difference. Our two-step algorithm first uses invariance tests between regression coefficients of the two data sets to estimate the skeleton of the difference graph and then orients some of the edges using invariance tests between regression residual variances. We demonstrate the properties of our method through a simulation study and apply it to the analysis of gene expression data from ovarian cancer and during T-cell activation.
MIT Department
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://papers.nips.cc/paper/7634-direct-estimation-of-differences-in-causal-graphs
