Replication Data for: Causal Inference with Latent Treatments

Social scientists are interested in the effects of low-dimensional latent treatments within texts, such as the effect of an attack on a candidate in a political advertisement. We provide a framework for causal inference with latent treatments in high-dimensional interventions. Using this framework, we show that the randomization of texts alone is insufficient to identify the causal effects of latent treatments, because other unmeasured treatments in the text could confound the measured treatment's effect. We provide a set of assumptions that is sufficient to identify the effect of latent treatments and a set of strategies to make these assumptions more plausible, including explicitly adjusting for potentially confounding text features and non-traditional experimental designs involving many versions of the text. We apply our framework to a survey experiment and an observational study, demonstrating how our framework makes text-based causal inferences more credible.社会科学家一直关注文本内部的低维潜在干预（low-dimensional latent treatments）效应，例如政治广告中针对候选人的攻击式表述所产生的因果效应。我们提出了一套面向高维干预场景下潜在干预变量的因果推断框架。基于该框架，我们证明仅对文本进行随机化分配，不足以识别潜在干预变量的因果效应，这是因为文本中其他未被测量的干预可能会对所观测到的干预的效应产生混杂偏倚。我们提出了一套足以识别潜在干预变量效应的假设条件，以及一系列可增强这些假设合理性的策略，包括针对潜在混杂文本特征进行显式调整，以及采用包含多种文本变体的非传统实验设计。我们将所提框架应用于一项调查实验与一项观察性研究，展示了该框架如何提升基于文本的因果推断的可信度。