01336nam a2200169 a 4500 20260901030416.0 250101s2019 xx o 000 0 eng d Basil Saeed Anchored Causal Inference in the Presence of Measurement Error arXiv 2019 text online resource 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. Open access — freely available to read. https://arxiv.org/pdf/1906.00928v1 Read the full paper (PDF) ERES 715 715 0 0 0 3 GEN MAIN MAIN SCICOMP 2026-09-01 0 YGE000976 2026-09-01 03:04:16 https://arxiv.org/pdf/1906.00928v1 2026-09-01 PAPER