Dotears: Scalable and consistent directed acyclic graph estimation using observational and interventional data.

ABSTRACT: New assays such as Perturb-seq link parallel CRISPR interventions to transcriptomic readouts, providing insight into gene regulatory networks. Causal regulatory networks can be represented by directed acyclic graphs (DAGs), but lack of identifiability and a combinatorial solution space complicate learning DAGs from observational data. Score-based methods have improved the practical scalability of inferring DAGs, but are sensitive to error variance structure. Furthermore, correction for error variance is difficult without prior knowledge of structure. We present dotears [doo-tairs], a continuous optimization framework leveraging observational and interventional data to infer causal structure, assuming a linear Structural Equation Model. dotears exploits structural consequences of hard inter

SUBMITTER: Xue A 

PROVIDER: S-EPMC11814701 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature