论文标题
样本选择下的异质治疗效果界限，并应用社交媒体对政治两极分化的影响
Heterogeneous Treatment Effect Bounds under Sample Selection with an Application to the Effects of Social Media on Political Polarization
论文作者
论文摘要
我们提出了一种用于估计和推断界限的方法，用于一般样本选择模型中的异质因果效应参数，在该模型中，治疗可以影响是否观察到结果并且没有排除限制。该方法提供了条件效应界限作为策略相关的预处理变量的功能。它允许对身份不明的条件效应进行有效的统计推断。我们使用灵活的DEBIAS/双机器学习方法，可以适应非线性功能形式和高维混杂因素。还提供了易于验证的高级条件，以供估计，错误指定稳健的置信区间和均匀的置信带。我们从Facebook上的大规模现场实验中重新分析数据，并在patientudinal的新闻订阅中进行了逐步订阅。与常规方法相比，我们的方法产生的效果范围更高，并暗示对年轻用户的去极化影响。
We propose a method for estimation and inference for bounds for heterogeneous causal effect parameters in general sample selection models where the treatment can affect whether an outcome is observed and no exclusion restrictions are available. The method provides conditional effect bounds as functions of policy relevant pre-treatment variables. It allows for conducting valid statistical inference on the unidentified conditional effects. We use a flexible debiased/double machine learning approach that can accommodate non-linear functional forms and high-dimensional confounders. Easily verifiable high-level conditions for estimation, misspecification robust confidence intervals, and uniform confidence bands are provided as well. We re-analyze data from a large scale field experiment on Facebook on counter-attitudinal news subscription with attrition. Our method yields substantially tighter effect bounds compared to conventional methods and suggests depolarization effects for younger users.
