Cross-document event coreference resolution (CD-ECR) focuses on identifying whether descriptions of events found in different documents actually refer to the same event in the real world. However, current CD-ECR approaches predominantly rely on trigger features within input mention pairs, which induce spurious correlations between surface-level lexical features and coreference relationships, impairing the overall performance of the models. To address this issue, we propose Debiasing Cross-Document Event Coreference Resolution via Counterfactual Reasoning (DCCR), a framework that mitigates spurious correlations in CD-ECR by leveraging causal interventions and counterfactual reasoning. DCCR constructs a structural causal graph to capture the confounding effects introduced by trigger word matching and applies interventions to isolate the true causal impact of these confounding factors. Additionally, a counterfactual reasoning module quantifies the impact of hypothetical perturbations to trigger words, allowing the model to disentangle superficial correlations from causally relevant features. DCCR offers a principled and end-to-end approach to mitigate bias without altering the training data. Experimental results on benchmark datasets demonstrate that our method significantly outperforms state-of-the-art baselines.