Permutation-based Causal Inference Algorithms with Interventions

NeurIPS 2017 Yuhao WangLiam SolusKarren YangCaroline Uhler

Learning directed acyclic graphs using both observational and interventional data is now a fundamentally important problem due to recent technological developments in genomics that generate such single-cell gene expression data at a very large scale. In order to utilize this data for learning gene regulatory networks, efficient and reliable causal inference algorithms are needed that can make use of both observational and interventional data... (read more)

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