Causal discovery of genome-scale networks is important for identifying pathways from genes to observable traits - e.g. differences in cell function, disease, drug resistance and others. Causal learners based on graphical models rely on interventional samples to orient edges in the network. However, these models have not been shown to scale up the size of the genome, which are on the order of 1e3-1e4 genes. We introduce a new learner, SP-GIES, that jointly learns from interventional and observational datasets and achieves almost 4x speedup against an existing learner for 1,000 node networks. SP-GIES achieves an AUC-PR score of 0.91 on 1,000 node networks, and scales up to 2,000 node networks - this is 4x larger than existing works. We also show how SP-GIES improves downstream optimal experimental design strategies for selecting interventional experiments to perform on the system. This is an important step forward in realizing causal discovery at scale via autonomous experimental design.

翻译：基因组规模网络的因果发现对于识别从基因到可观测性状（如细胞功能差异、疾病、耐药性等）的途径至关重要。基于图模型的因果学习器依赖于干预样本对网络中的边进行定向。然而，这些模型尚未被证明能够扩展到基因组规模（约1e3-1e4个基因）。我们提出了一种新的学习器SP-GIES，它能够联合学习干预数据集和观测数据集，并在1000节点网络上相比现有学习器实现了近4倍的加速。SP-GIES在1000节点网络上的AUC-PR得分达到0.91，并可扩展至2000节点网络——这比现有工作大了4倍。我们还展示了SP-GIES如何改进下游的最优实验设计策略，用于选择对系统进行的干预实验。这是通过自主实验设计实现规模化因果发现的重要进展。