• Corpus ID: 238226888

# Causal Matrix Completion

@article{Agarwal2021CausalMC,
title={Causal Matrix Completion},
author={Anish Agarwal and Munther A. Dahleh and Devavrat Shah and Dennis Shen},
journal={ArXiv},
year={2021},
volume={abs/2109.15154}
}
• Published 30 September 2021
• Computer Science, Mathematics
• ArXiv
Matrix completion is the study of recovering an underlying matrix from a sparse subset of noisy observations. Traditionally, it is assumed that the entries of the matrix are “missing completely at random” (MCAR), i.e., each entry is revealed at random, independent of everything else, with uniform probability. This is likely unrealistic due to the presence of “latent confounders”, i.e., unobserved factors that determine both the entries of the underlying matrix and the missingness pattern in the…

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