Many state‑of‑the‑art anomaly detection models operate as black boxes, limiting interpretability and hindering reliable deployment. While recent advances in explainable artificial intelligence have focused on explaining why individual instances are detected as anomalous, comparatively little attention has been paid to how such explanations can be systematically exploited to improve the detectors themselves. To address this gap, we propose EAD‑CE (Enhancing Anomaly Detection with Counterfactual Explanations), a model‑agnostic, closed‑loop framework that tightly integrates detection, explanation, and improvement. Specifically, given a trained anomaly detector and its detected anomalies, EAD‑CE generates minimal and semantically meaningful counterfactual explanations that reveal how targeted feature perturbations influence anomaly scores or decisions. Feature importance inferred from these counterfactual explanations is then used to guide a dynamic feature-weight optimization process, enabling detector refinement without modifying its underlying architecture. Extensive experiments on nine real‑world datasets and three anomaly detection models demonstrate that EAD‑CE accurately identifies anomaly‑driving features, substantially enhances interpretability, and consistently improves detection performance (average AUC increases by 6.9%, up to 23%). Implementation details are provided to support reproducibility.