论文标题
对弱或无效仪器的鲁棒性：通过机器学习探索非线性治疗模型
Robustness Against Weak or Invalid Instruments: Exploring Nonlinear Treatment Models with Machine Learning
论文作者
论文摘要
我们讨论了可能是无效仪器变量的观察性研究的因果推断。我们通过使用机器学习探索非线性处理模型，提出了一种称为两阶段曲率识别（TSCI）的新方法。 {第一阶段的机器学习能够改善仪器变量的强度并调整不同形式的违反仪器变量假设的形式。} TSCI的成功要求仪器变量对治疗的影响与其违规形式有所不同。实施了一个新的偏见校正步骤，以消除机器学习的潜在复杂性而导致的偏见。我们提出的\ texttt {tsci}估计量被证明是渐近无偏见和高斯，即使机器学习算法并未始终如一地估计治疗模型。此外，我们设计了一种与数据相关的方法，可以在几种候选侵犯表格中选择最佳方法。我们应用TSCI来研究教育对收入的影响。
We discuss causal inference for observational studies with possibly invalid instrumental variables. We propose a novel methodology called two-stage curvature identification (TSCI) by exploring the nonlinear treatment model with machine learning. {The first-stage machine learning enables improving the instrumental variable's strength and adjusting for different forms of violating the instrumental variable assumptions.} The success of TSCI requires the instrumental variable's effect on treatment to differ from its violation form. A novel bias correction step is implemented to remove bias resulting from the potentially high complexity of machine learning. Our proposed \texttt{TSCI} estimator is shown to be asymptotically unbiased and Gaussian even if the machine learning algorithm does not consistently estimate the treatment model. Furthermore, we design a data-dependent method to choose the best among several candidate violation forms. We apply TSCI to study the effect of education on earnings.
