Problem or Issue	What is Already Known	What this Paper Adds
The diversity of causal discovery algorithms and the presence of cyclic structures in synthesized causal networks.	No single causal method exhibits significantly superior performance in structure reconstruction. The existence of cyclic structures is often disregarded when synthesizing network knowledge from different sources, which may result in erroneous inferences.	We proposed a novel network harmonization framework that effectively integrates causal graphs from different causal algorithms. To address the cyclic structure in network fusion, we developed a novel cycle-breaking algorithm. Experimental results showed that our model significantly outperformed baseline models on both synthetic data and real-world data. The model has the potential to guide the development of personalized treatment strategies.