Optimal adjustment sets for causal query estimation in partially observed biomolecular networks

Journal Article · 30 June 2023 · Bioinformatics

DOI: https://doi.org/10.1093/bioinformatics/btad270 · OSTI ID:1987738

Mohammad-Taheri, Sara; Tewari, Vartika; Kapre, Rohan; Rahiminasab, Ehsan; Sachs, Karen; Tapley Hoyt, Charles; Zucker, Jeremy; Vitek, Olga

Abstract Causal query estimation in biomolecular networks commonly selects a ‘valid adjustment set’, i.e. a subset of network variables that eliminates the bias of the estimator. A same query may have multiple valid adjustment sets, each with a different variance. When networks are partially observed, current methods use graph-based criteria to find an adjustment set that minimizes asymptotic variance. Unfortunately, many models that share the same graph topology, and therefore same functional dependencies, may differ in the processes that generate the observational data. In these cases, the topology-based criteria fail to distinguish the variances of the adjustment sets. This deficiency can lead to sub-optimal adjustment sets, and to miss-characterization of the effect of the intervention. We propose an approach for deriving ‘optimal adjustment sets’ that takes into account the nature of the data, bias and finite-sample variance of the estimator, and cost. It empirically learns the data generating processes from historical experimental data, and characterizes the properties of the estimators by simulation. We demonstrate the utility of the proposed approach in four biomolecular Case studies with different topologies and different data generation processes. The implementation and reproducible Case studies are at https://github.com/srtaheri/OptimalAdjustmentSet.

• Research Organization: Univ. of Washington, Seattle, WA (United States)
• Sponsoring Organization: Defense Advanced Research Projects Agency (DARPA); National Institutes of Health (NIH); USDOE; USDOE Office of Science (SC), Biological and Environmental Research (BER)
• Grant/Contract Number: SC0023091
• OSTI ID: 1987738
• Journal Information: Bioinformatics, Journal Name: Bioinformatics Journal Issue: Supplement_1 Vol. 39; ISSN 1367-4803
• Publisher: Oxford University PressCopyright Statement
• Country of Publication: United Kingdom
• Language: English