000			01336nam a2200169 a 4500
005			20260901030416.0
008			250101s2019 xx o 000 0 eng d
100	1		_aBasil Saeed
245	1	0	_aAnchored Causal Inference in the Presence of Measurement Error
264		1	_barXiv _c2019
336			_atext
338			_aonline resource
520			_aWe 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		_aOpen access — freely available to read.
856	4	0	_uhttps://arxiv.org/pdf/1906.00928v1 _yRead the full paper (PDF)
942			_cERES
999			_c715 _d715