Beyond counterfactual detection, paraphrase identification, and implicit sentiment analysis, several other bias-sensitive NLP tasks could benefit from the proposed debiasing techniques. For instance, sentiment analysis could be enhanced by addressing biases related to specific phrases or cultural contexts that may skew sentiment predictions. Named entity recognition (NER) is another area where biases can affect the identification of entities based on gender, race, or other attributes; applying causal intervention could help create a more balanced representation of entities. Additionally, text classification tasks, particularly those involving sensitive topics such as hate speech detection or misinformation classification, could leverage these techniques to reduce the impact of biased training data. Finally, dialog systems could also benefit, as biases in conversational data can lead to inappropriate or unbalanced responses. By integrating causal intervention frameworks, these tasks can achieve improved fairness and accuracy in their predictions, ultimately leading to more reliable NLP applications.