Causal relationships play a crucial role in enhancing decision-making processes and optimizing system design. In real-world scenarios, different tasks introduce uncertainty in optimization objectives due to their varying requirements. Some tasks aim to discover as many causal relationships as possible, while others require highly precise causal relationships to make reliable decisions. Existing methods are confined to learning a fixed causal structure, thereby failing to adapt to these diverse requirements. To address this challenge, we propose a novel framework called Three-Way Causal Structure Representation (TW-CSR). First, we represent the framework of TW-CSR as (DCE, UCE, IN). Then, in two propositions, we discuss the relationship between precision and recall in causal structures, providing a theoretical foundation for selecting an appropriate causal graph structure to meet task requirements. Finally, based on the propositions, we propose causal structure learning methods under the TW-CSR framework for both constraint-based and score-based approaches. Experimental results demonstrate that our framework and methods effectively address the uncertainty introduced by diverse tasks.