We consider the problem of estimating causal DAG models from a mix of
observational and interventional data, when the intervention targets are
partially or completely unknown. This problem is highly relevant for example in
genomics, since gene knockout technologies are known to have off-target
effects. We characterize the interventional Markov equivalence class of DAGs
that can be identified from interventional data with unknown intervention
targets. In addition, we propose a provably consistent algorithm for learning
the interventional Markov equivalence class from such data. The proposed
algorithm greedily searches over the space of permutations to minimize a novel
score function. The algorithm is nonparametric, which is particularly important
for applications to genomics, where the relationships between variables are
often non-linear and the distribution non-Gaussian. We demonstrate the
performance of our algorithm on synthetic and biological datasets. Links to an
implementation of our algorithm and to a reproducible code base for our
experiments can be found at this https URL

针对部分或完全未知的干预目标的混合观测和干预数据的因果 DAG 模型估计问题，本文提出一个算法来寻找干预马可夫等价类，此算法采用贪心策略在排列空间中搜索以最小化新的分数函数，同时该算法是无参数的，因此适用于非线性关系和非高斯分布的情况，性能表现已被验证。