Experimental design for causal query estimation in partially observed biomolecular networks

TL;DR

This paper introduces a simulation-based method for selecting optimal sub-networks in biomolecular systems to improve causal query estimation accuracy while reducing costs and computational complexity.

Contribution

It proposes a novel algorithm for choosing sub-networks that support unbiased causal estimators under cost constraints, enhancing estimation efficiency.

Findings

Effective sub-network selection improves estimation accuracy.

Case studies demonstrate practical benefits of the method.

Reproducible results available online.

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.