The issue localization task aims to identify the locations in a software repository that requires modification given a natural language issue description. This task is fundamental yet challenging in automated software engineering due to the semantic gap between issue description and source code implementation. This gap manifests as two mismatches: (1) \emph{symptom–to-cause mismatches}, where descriptions do not explicitly reveal underlying root causes; (2) \emph{one-to-many mismatches}, where a single issue corresponds to multiple interdependent code entities. To address these two mismatches, we propose \emph{GraphLocator}, an LLM-based approach that mitigates symptom–to-cause mismatches through \emph{causal structure discovering} and resolves one-to-many mismatches via \emph{dynamic issue disentangling}. The key artifact of \emph{GraphLocator} is the \emph{causal issue graph} (CIG), in which vertices represent discovered sub-issues along with their associated code entities, and edges encode the causal dependencies between them. The workflow of \emph{GraphLocator} consists of two phases: \emph{symptom vertices locating} and \emph{dynamic CIG discovering}; it first identifies symptom locations on the repository graph, then dynamically expands the CIG by iteratively reasoning over neighboring vertices, discovering new sub-issues and updating causal dependencies. Experiments on three real-world Python and Java datasets demonstrates the effectiveness of \emph{GraphLocator}: (1) Compared with baselines, \emph{GraphLocator} achieves more accurate localization with average improvements of +19.49% in function-level recall and +11.89% in precision with acceptable overhead. (2) \emph{GraphLocator} outperforms baselines on both symptom-to-cause and one-to-many mismatch scenarios, achieving recall improvement of +16.44% and +19.18%, precision improvement of +7.78% and +13.23%, respectively. (3) The disentangled causal structure CIG generated by \emph{GraphLocator} yields the highest relative improvement, resulting in a 28.74% increase in performance on the downstream issue-resolving task.