dc.contributor.author | Wang, Yuhao | |
dc.contributor.author | Solus, Liam | |
dc.contributor.author | Yang, Karren Dai | |
dc.contributor.author | Uhler, Caroline | |
dc.date.accessioned | 2021-11-08T18:16:43Z | |
dc.date.available | 2021-11-08T18:16:43Z | |
dc.date.issued | 2017 | |
dc.identifier.uri | https://hdl.handle.net/1721.1/137756 | |
dc.description.abstract | © 2017 Neural information processing systems foundation. All rights reserved. Learning directed acyclic graphs using both observational and interventional data is now a fundamentally important problem due to recent technological developments in genomics that generate such single-cell gene expression data at a very large scale. In order to utilize this data for learning gene regulatory networks, efficient and reliable causal inference algorithms are needed that can make use of both observational and interventional data. In this paper, we present two algorithms of this type and prove that both are consistent under the faithfulness assumption. These algorithms are interventional adaptations of the Greedy SP algorithm and are the first algorithms using both observational and interventional data with consistency guarantees. Moreover, these algorithms have the advantage that they are nonparametric, which makes them useful also for analyzing non-Gaussian data. In this paper, we present these two algorithms and their consistency guarantees, and we analyze their performance on simulated data, protein signaling data, and single-cell gene expression data. | en_US |
dc.language.iso | en | |
dc.rights | 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. | en_US |
dc.source | Neural Information Processing Systems (NIPS) | en_US |
dc.title | Permutation-based Causal Inference Algorithms with Interventions | en_US |
dc.type | Article | en_US |
dc.identifier.citation | Wang, Yuhao, Solus, Liam, Yang, Karren Dai and Uhler, Caroline. 2017. "Permutation-based Causal Inference Algorithms with Interventions." | |
dc.contributor.department | Massachusetts Institute of Technology. Laboratory for Information and Decision Systems | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Institute for Data, Systems, and Society | en_US |
dc.eprint.version | Final published version | en_US |
dc.type.uri | http://purl.org/eprint/type/ConferencePaper | en_US |
eprint.status | http://purl.org/eprint/status/NonPeerReviewed | en_US |
dc.date.updated | 2019-07-09T17:29:29Z | |
dspace.date.submission | 2019-07-09T17:29:30Z | |
mit.metadata.status | Authority Work and Publication Information Needed | en_US |