Abstract: We consider the problem of reducing the false discovery rate in multiple high-dimensional interventional datasets under unknown intervention targets when the time efficiency is acceptable. Traditional algorithms merged directly multiple causal DAGs learned, which ignores the contradictions of different datasets, leading to lots of contradictory directions of edges. For reducing the contradictory information, we propose a new algorithm, which first learns an intervention Markov equivalence class (I-MEC) before merging multiple causal DAGs. It utilizes the full power of the constraints available in interventional data and combines ideas from local learning, intervention, and search-and-score techniques in a principled and effective way in different intervention experiments. Specifically, local learning on multiple datasets is used to build a causal skeleton. Perfect intervention destroys some possible triangles and obtains more V-structures, getting a theoretically correct I-MEC. Search and scoring techniques based on the learned I-MEC further identify the remaining unoriented edges. Experiments on benchmark Bayesian networks with the number of variables from 20 to 724 validate that the effectiveness of our algorithm in reducing the false discovery rate in high-dimensional interventional data.