Title: Counterfactual Thinking for Long-tailed Information Extraction. Abstract: Information Extraction (IE) aims to extract structured information from unstructured texts. However, in practice, the long-tailed and imbalanced data may lead to severe bias issues for deep learning models, due to very few training instances available for the tail classes. Existing works are mainly from computer vision society,  leveraging re-balancing, decoupling, transfer learning and causal inference to address this problem on image classification and scene graph generation. However, these approaches may not achieve good performance on textual data, which involves complex language structures that have been proven crucial for the IE tasks. To this end, we propose a novel framework (named CFIE) based on language structure and causal reasoning with three key ingredients. First, by fusing the syntax information to various structured causal models for mainstream IE tasks including relation extraction (RE), named entity recognition (NER), and event detection (ED), our approach is able to learn the direct effect for classification from an imbalanced dataset. Second, counterfactuals are generated based on an explicit language structure to better calculate the direct effect during the inference stage. Third, we propose a flexible debiasing approach for more robust prediction during the inference stage. Experimental results on three IE tasks across five public datasets show that our model significantly outperforms the state-of-the-art models by a large margin in terms of Mean Recall and Macro F1, achieving a relative 30% improvement in Mean Recall for 7 tail classes on the  ACE2005 dataset. We also discuss some interesting findings based on our observations.