Anchored Causal Inference in the Presence of Measurement Error (Record no. 715)

000 -LEADER
fixed length control field 01336nam a2200169 a 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20260901030416.0
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fixed length control field 250101s2019 xx o 000 0 eng d
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Basil Saeed
245 10 - TITLE STATEMENT
Title Anchored Causal Inference in the Presence of Measurement Error
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Name of producer, publisher, distributor, manufacturer arXiv
Date of production, publication, distribution, manufacture, or copyright notice 2019
336 ## - CONTENT TYPE
Content type term text
338 ## - CARRIER TYPE
Carrier type term online resource
520 ## - SUMMARY, ETC.
Summary, etc. We consider the problem of learning a causal graph in the presence of measurement error. This setting is for example common in genomics, where gene expression is corrupted through the measurement process. We develop a provably consistent procedure for estimating the causal structure in a linear Gaussian structural equation model from corrupted observations on its nodes, under a variety of measurement error models. We provide an estimator based on the method-of-moments, which can be used in conjunction with constraint-based causal structure discovery algorithms. We prove asymptotic consistency of the procedure and also discuss finite-sample considerations. We demonstrate our method's performance through simulations and on real data, where we recover the underlying gene regulatory network from zero-inflated single-cell RNA-seq data.
506 0# - RESTRICTIONS ON ACCESS NOTE
Terms governing access Open access — freely available to read.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://arxiv.org/pdf/1906.00928v1">https://arxiv.org/pdf/1906.00928v1</a>
Link text Read the full paper (PDF)
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Electronic resource (link)
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      Available online General Yegates University Library Yegates University Library Science and Computing 09/01/2026   YGE000976 09/01/2026 https://arxiv.org/pdf/1906.00928v1 09/01/2026 Research paper — read online