Anchored causal inference in the presence of measurement error
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saeed20a.pdf
Description
Published version
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871.34 KB
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Adobe PDF
Checksum (MD5)
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Author(s)
Saeed, B
Belyaeva, A
Wang, Y
Uhler, C
Date Issued
January 1, 2020
Journal
Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence, UAI 2020
Citation
Saeed, B, Belyaeva, A, Wang, Y and Uhler, C. 2020. "Anchored causal inference in the presence of measurement error." Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence, UAI 2020, 124.
Version
Final published version
Abstract
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. Namely, we provide an estimator based on the method-of-moments and an associated test 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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
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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://proceedings.mlr.press/v124/saeed20a.html
