Title: Counterfactual Contrastive Learning for Robust Text Classification. Abstract: Text classification has recently been promoted by large pre-trained language models (PLMs) which aim to identify target classes with knowledge transferred from sets of reading comprehension tasks. However, derivative models of PLMs still suffer from sensitive performance on different datasets, the reasons are multiple such as cross-domain and label imbalance problems, from which most models may learn the spurious correlation between texts and labels. Existing research requires people to manually add counterfactual samples to the dataset or automatically match so-called counterfactual pairs that are already in the dataset for augmentation. In this paper, we propose a novel LDA-based counterfactual contrastive learning framework and three data augmentation methods, to capture the causal information in texts, which can promote the robustness of text classification. To confirm the effectiveness of our proposed model and methods, we design and conduct several couples of experiments. Experimental results demonstrate that our model works well on five popular text classification datasets on distinct tasks, we find that training with proposed data augmentation outperforms other augmentation methods on many superior models by 1\% or above. Plus, robustness tests on different datasets also show a competitive performance, which proves the effectiveness of our model and data.