基于文本的因果推断中检测与缓解处理泄漏：蒸馏与敏感性分析
Detecting and Mitigating Treatment Leakage in Text-Based Causal Inference: Distillation and Sensitivity Analysis
AI总结：
本文提出了一种基于文本的因果推断中检测和缓解处理泄漏的方法，通过蒸馏和敏感性分析来减少偏倚并提高估计精度。
摘要：
基于文本的因果推断越来越多地使用文本数据作为未观察到的混杂因素的代理，但这种方法引入了一个此前未被理论化的偏倚来源：处理泄漏。处理泄漏发生在文本旨在捕捉混杂信息时，也包含预测治疗状态的信号，从而在因果估计中引入后处理偏倚。关键的是，这个问题即使在文档在治疗分配之前出现时也可能发生，因为作者可能使用未来参考语言来预测后续干预。尽管对这个问题的认识逐渐增加，但目前还没有系统的方法来识别和缓解文本作为混杂因素应用中的处理泄漏。本文通过三个贡献来填补这一空白。首先，我们提供了处理泄漏的正式统计和集合论定义，以澄清何时以及为何出现偏倚。其次，我们提出了四种文本蒸馏方法——基于相似性的段落移除、远距离监督分类、显著特征移除和迭代空域投影——旨在消除预测治疗内容的同时保留混杂信息。第三，我们通过使用合成文本和一个实证应用来验证这些方法，该应用研究了国际货币基金组织结构调整计划和儿童死亡率。我们的发现表明，适度的蒸馏在减少偏倚和保留混杂信息之间达到最佳平衡，而过于严格的处理方法会降低估计的精度。
英文摘要：
Text-based causal inference increasingly employs textual data as proxies for unobserved confounders, yet this approach introduces a previously undertheorized source of bias: treatment leakage. Treatment leakage occurs when text intended to capture confounding information also contains signals predictive of treatment status, thereby inducing post-treatment bias in causal estimates. Critically, this problem can arise even when documents precede treatment assignment, as authors may employ future-referencing language that anticipates subsequent interventions. Despite growing recognition of this issue, no systematic methods exist for identifying and mitigating treatment leakage in text-as-confounder applications. This paper addresses this gap through three contributions. First, we provide formal statistical and set-theoretic definitions of treatment leakage that clarify when and why bias occurs. Second, we propose four text distillation methods -- similarity-based passage removal, distant supervision classification, salient feature removal, and iterative nullspace projection -- designed to eliminate treatment-predictive content while preserving confounder information. Third, we validate these methods through simulations using synthetic text and an empirical application examining International Monetary Fund structural adjustment programs and child mortality. Our findings indicate that moderate distillation optimally balances bias reduction against confounder retention, whereas overly stringent approaches degrade estimate precision.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
