Counterfactual explanations for recommender systems aim to identify minimal changes to a user's interaction history that would alter the model's recommendation, thereby providing actionable and user-centric explanations. However, existing CE methods often rely on local perturbation or mask-based strategies that require substantial profile modifications and exhibit unstable behavior under fine-grained ranking changes. In this work, we propose DiceRec, a diffusion-based, model-agnostic framework for generating interaction-level counterfactual explanations. DiceRec leverages denoising diffusion probabilistic models to learn the distribution of user interaction histories and integrates counterfactual objectives directly into the reverse diffusion process via guided denoising. By shaping the denoising trajectory at each step, DiceRec generates compact, semantically coherent counterfactuals that induce the desired ranking changes without relying on explicit sparsity regularization or heuristic perturbation budgets. Beyond the method itself, we introduce a fine-grained and constrained evaluation protocol that reveals important differences in explanation quality that are obscured by conventional coarse-grained, percentage-based perturbation schemes. Through extensive experiments, we show that DiceRec consistently outperforms state-of-the-art baselines, particularly in minimal-perturbation scenarios that reflect realistic counterfactual conditions. Our results highlight the advantages of diffusion-based counterfactual generation and underscore the necessity of fine-grained evaluation for faithful explanation assessment in recommender systems.