Anchored Causal Inference in the Presence of Measurement Error

Basil Saeed, Anastasiya Belyaeva, Yuhao Wang, Caroline Uhler
Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), PMLR 124:619-628, 2020.

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. 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v124-saeed20a, title = {Anchored Causal Inference in the Presence of Measurement Error}, author = {Saeed, Basil and Belyaeva, Anastasiya and Wang, Yuhao and Uhler, Caroline}, booktitle = {Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI)}, pages = {619--628}, year = {2020}, editor = {Peters, Jonas and Sontag, David}, volume = {124}, series = {Proceedings of Machine Learning Research}, month = {03--06 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v124/saeed20a/saeed20a.pdf}, url = {https://proceedings.mlr.press/v124/saeed20a.html}, 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. 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.} }
Endnote
%0 Conference Paper %T Anchored Causal Inference in the Presence of Measurement Error %A Basil Saeed %A Anastasiya Belyaeva %A Yuhao Wang %A Caroline Uhler %B Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI) %C Proceedings of Machine Learning Research %D 2020 %E Jonas Peters %E David Sontag %F pmlr-v124-saeed20a %I PMLR %P 619--628 %U https://proceedings.mlr.press/v124/saeed20a.html %V 124 %X 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.
APA
Saeed, B., Belyaeva, A., Wang, Y. & Uhler, C.. (2020). Anchored Causal Inference in the Presence of Measurement Error. Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), in Proceedings of Machine Learning Research 124:619-628 Available from https://proceedings.mlr.press/v124/saeed20a.html.

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