BibTeX

@article{2210.13423v2,
Author        = {Sara Mohammad-Taheri and Vartika Tewari and Rohan Kapre and Ehsan Rahiminasab and Karen Sachs and Charles Tapley Hoyt and Jeremy Zucker and Olga Vitek},
Title         = {Experimental design for causal query estimation in partially observed
  biomolecular networks},
Eprint        = {2210.13423v2},
ArchivePrefix = {arXiv},
PrimaryClass  = {q-bio.BM},
Abstract      = {Estimating a causal query from observational data is an essential task in the
analysis of biomolecular networks. Estimation takes as input a network
topology, a query estimation method, and observational measurements on the
network variables. However, estimations involving many variables can be
experimentally expensive, and computationally intractable. Moreover, using the
full set of variables can be detrimental, leading to bias, or increasing the
variance in the estimation. Therefore, designing an experiment based on a
well-chosen subset of network components can increase estimation accuracy, and
reduce experimental and computational costs. We propose a simulation-based
algorithm for selecting sub-networks that support unbiased estimators of the
causal query under a constraint of cost, ranked with respect to the variance of
the estimators. The simulations are constructed based on historical
experimental data, or based on known properties of the biological system. Three
case studies demonstrated the effectiveness of well-chosen network subsets for
estimating causal queries from observational data. All the case studies are
reproducible and available at https://github.com/srtaheri/Simplified_LVM.},
Year          = {2022},
Month         = {Oct},
Url           = {http://arxiv.org/abs/2210.13423v2},
File          = {2210.13423v2.pdf}
}