dotears: Scalable, consistent DAG estimation using observational and interventional data

arXiv:2305.19215v2 Announce Type: replace-cross
Abstract: New biological assays like Perturb-seq link highly parallel CRISPR interventions to a high-dimensional transcriptomic readout, providing insight into gene regulatory networks. Causal gene regulatory networks can be represented by directed acyclic graph (DAGs), but learning DAGs from observational data is complicated by lack of identifiability and a combinatorial solution space. Score-based structure learning improves practical scalability of inferring DAGs. Previous score-based methods are sensitive to error variance structure; on the other hand, estimation of error …

abstract arxiv consistent crispr cs.lg dag data gene graph insight networks regulatory scalable stat.ml type