Yes, the effectiveness of causal intervention can be demonstrated on generative language tasks such as machine translation and text summarization. In these tasks, biases can arise from the training data, leading to suboptimal outputs that do not accurately reflect the intended meaning or context. By applying causal intervention techniques, one can mitigate the influence of spurious correlations and biases present in the training data. For instance, in machine translation, causal intervention could help ensure that the model does not overly rely on specific phrases or structures that may not translate well across languages. Similarly, in text summarization, it could help the model focus on the most relevant content rather than being swayed by biased or irrelevant information. Implementing causal intervention in these contexts would involve adjusting the training objectives to account for the causal relationships between input features and output predictions, ultimately leading to more accurate and contextually appropriate generative outputs.