Causal Inference in Natural Language Processing: Application Status and Future Outlook

Abstract. Causal reasoning has received much attention in recent years. Unlike statistical learning that focuses on the correlation between variables, causal inference can analyze the causality between variables and avoid false causal relationships caused by confounding factors. The use of causal inference to help solve Natural Language Processing (NLP) problems has made great progress. Most existing studies have focused on exploring the application of causal relationships in downstream tasks of NLP, achieving good results. This article conducted a comprehensive survey, focusing on investigating the advantages of causal inference methods in solving specific NLP problems and applications, as well as their practical advantages compared to deep learning methods. Intended to provide researchers in the NLP field with more detailed perspectives and recommendations. Specifically, this article first introduces the relevant concepts of causal inference, including causal inference and related concepts of causal relationships, as well as the differences between causal inference and statistical machine learning; Then, existing research on the use of causal relationships in downstream applications was summarized, including bias removal, stock price prediction, document dialogue based, and continuous few shot learning. Finally, a summary was made on the development of future causal relationships.