用大模型分析文本干预对社交行为的影响，突破传统方法局限。
Estimating Causal Effects of Text Interventions Leveraging LLMs
- 借助大模型实现任意文本干预的因果效应估计
- 仅需对照组数据即可在领域偏移下保持稳定效果
- 适合研究社交媒体行为与文本干预的学者
量化社交系统中文本干预的效果（如减少社交媒体帖子中的愤怒情绪以观察其对互动的影响）极具挑战。现实世界中的干预往往不可行，因此依赖观测数据。传统因果推断方法通常针对二元或离散处理，难以应对复杂高维的文本数据。本文提出CausalDANN，一种利用大语言模型（LLMs）实现文本转换的新型因果效应估计方法。该方法可处理任意文本干预，并借助具备领域自适应能力的文本分类器，在仅观测到对照组的情况下仍能对领域偏移具有鲁棒性，从而获得可靠的效应估计。这一灵活性是文本数据因果推断的重要进展，为理解人类行为及设计有效干预提供了新可能。
原文摘要 · Abstract (English)
Quantifying the effects of textual interventions in social systems, such as reducing anger in social media posts to see its impact on engagement, is challenging. Real-world interventions are often infeasible, necessitating reliance on observational data. Traditional causal inference methods, typically designed for binary or discrete treatments, are inadequate for handling the complex, high-dimensional textual data. This paper addresses these challenges by proposing CausalDANN, a novel approach to estimate causal effects using text transformations facilitated by large language models (LLMs). Unlike existing methods, our approach accommodates arbitrary textual interventions and leverages text-level classifiers with domain adaptation ability to produce robust effect estimates against domain shifts, even when only the control group is observed. This flexibility in handling various text interventions is a key advancement in causal estimation for textual data, offering opportunities to better understand human behaviors and develop effective interventions within social systems.
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