文本与因果推理：用文本消除因果估计中的混淆
Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates
摘要：
计算社会科学的许多应用旨在从非实验数据中推断因果结论。这些观察数据通常包含混杂因素，即影响潜在原因和潜在影响的变量。未测量的或潜在的混杂因素可能会使因果估计产生偏差，这激发了人们对从观察到的文本中测量潜在混杂因素的兴趣。例如，一个人的整个社交媒体帖子历史或一篇新闻文章的内容可以提供多种混杂因素的丰富测量。然而，这个问题的方法和应用程序分散在不同的社区和评估实践是不一致的。这是第一次对这些例子进行收集和分类，并为数据处理和评价决策提供指导。尽管越来越多的关注调整混杂使用文本，仍然有许多开放的问题，我们在本文中强调。
英文摘要：
Many applications of computational social science aim to infer causal conclusions from non-experimental data. Such observational data often contains confounders, variables that influence both potential causes and potential effects. Unmeasured or latent confounders can bias causal estimates, and this has motivated interest in measuring potential confounders from observed text. For example, an individual's entire history of social media posts or the content of a news article could provide a rich measurement of multiple confounders. Yet, methods and applications for this problem are scattered across different communities and evaluation practices are inconsistent. This review is the first to gather and categorize these examples and provide a guide to data-processing and evaluation decisions. Despite increased attention on adjusting for confounding using text, there are still many open problems, which we highlight in this paper.
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。
