Anchored Causal Inference in the Presence of Measurement Error
Basil Saeed, Anastasiya Belyaeva, Yuhao Wang, Caroline Uhler
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.
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.}
}