<p>Causal Inference (CI) plays a critical role in building unbiased recommender systems. However, most existing CI-based debiasing methods primarily pay attention to either popularity bias or conformity bias. In this paper, we propose a Disentangled Counterfactual Reasoning framework to alleviate dual biases in recommendation, called DCR. To be specific, in the training stage, we incorporate the impact of item popularity and user conformity to fit the biased recommendation process, and separate their indirect effects by disentangling user and item embeddings into biased and unbiased components. In the inference stage, we perform counterfactual reasoning to intervene in the item scoring process, which simultaneously mitigates the direct and indirect effects of bias factors. Extensive experiments on four widely used datasets demonstrate that DCR significantly outperforms existing debiasing methods with an average improvement of 9.2% in Recall and 9.5% in NDCG, and exhibits remarkable capability in alleviating both popularity bias and conformity bias.</p>