RobustIV 和 controlfunctionIV：针对无效工具变量的线性和非线性模型的因果推断
RobustIV and controlfunctionIV: Causal Inference for Linear and Nonlinear Models with Invalid Instrumental Variables
AI总结：
本文介绍两个R软件包RobustIV和controlfunctionIV，分别用于线性与非线性模型下存在无效工具变量时的因果推断，并提供了经济数据和弗拉明汉心脏研究的应用示例。
摘要：
我们介绍了用于处理可能无效的工具变量的因果推断的 R 软件包 RobustIV 和 controlfunctionIV。RobustIV 专注于线性结果模型。它实现了两阶段硬阈值方法，从一组候选工具变量中选择有效的工具变量，并在低维和高维设置中对因果效应进行推断。此外，RobustIV 还实现了高维内生性检验以及搜索和抽样方法，这是一种对工具变量选择错误具有鲁棒性的统一有效推断方法。controlfunctionIV 考虑非线性结果模型，并基于控制函数方法对因果效应进行推断。我们的软件包通过两个公开可用的经济数据集以及弗拉明汉心脏研究的应用进行了演示。
英文摘要：
We present R software packages RobustIV and controlfunctionIV for causal inference with possibly invalid instrumental variables. RobustIV focuses on the linear outcome model. It implements the two-stage hard thresholding method to select valid instrumental variables from a set of candidate instrumental variables and make inferences for the causal effect in both low- and high-dimensional settings. Furthermore, RobustIV implements the high-dimensional endogeneity test and the searching and sampling method, a uniformly valid inference method robust to errors in instrumental variable selection. controlfunctionIV considers the nonlinear outcome model and makes inferences about the causal effect based on the control function method. Our packages are demonstrated using two publicly available economic data sets together with applications to the Framingham Heart Study.
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