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The paper introduces iTAG, a method for generating natural text with accurate causal graph annotations by framing the text generation process as an inverse problem, iteratively refining concept selection using Chain-of-Thought reasoning to align induced relations with the target causal graph. This approach addresses the trade-off between text naturalness and annotation accuracy present in existing methods. Experiments demonstrate iTAG achieves both high annotation accuracy and naturalness, producing data that correlates strongly with real-world data when used to benchmark text-based causal discovery algorithms.
iTAG flips the script on causally-annotated text generation, achieving both natural language and high annotation accuracy by treating the causal graph as the target and iteratively refining concept selection.
A fundamental obstacle to causal discovery from text is the lack of causally annotated text data for use as ground truth, due to high annotation costs. This motivates an important task of generating text with causal graph annotations. Early template-based generation methods sacrifice text naturalness in exchange for high causal graph annotation accuracy. Recent Large Language Model (LLM)-dependent methods directly generate natural text from target graphs through LLMs, but do not guarantee causal graph annotation accuracy. Therefore, we propose iTAG, which performs real-world concept assignment to nodes before converting causal graphs into text in existing LLM-dependent methods. iTAG frames this process as an inverse problem with the causal graph as the target, iteratively examining and refining concept selection through Chain-of-Thought (CoT) reasoning so that the induced relations between concepts are as consistent as possible with the target causal relationships described by the causal graph. iTAG demonstrates both extremely high annotation accuracy and naturalness across extensive tests, and the results of testing text-based causal discovery algorithms with the generated data show high statistical correlation with real-world data. This suggests that iTAG-generated data can serve as a practical surrogate for scalable benchmarking of text-based causal discovery algorithms.
