This paper proposes a flexible new framework for constructing Neyman-orthogonal scores in semiparametric models involving infinite-dimensional nuisance parameters. While locally estimation is vital for integrating machine learning into econometrics, deriving orthogonal scores for complex models remains a major challenge. We provide explicit construction strategies for broad classes of settings. The proposed framework ensures asymptotic normality of target parameter estimators in a way that does not depend on the method used to construct the nuisance parameter estimators, provided they are $o_p(n^{-\1/4})$-consistent. We apply the proposed methodology to causal inference with a binary instrumental variable, developing a novel, robust estimator for treatment effects. Numerical studies demonstrate that our approach significantly outperforms naive alternatives in finite samples. An empirical application to the Oregon Health Insurance Experiment illustrates the framework's utility in providing robust causal evidence.

翻译：本文提出了一种灵活的新框架，用于构建涉及无穷维 nuisance 参数（nuisance parameters）的半参数模型中的内曼正交分数。尽管局部估计对于将机器学习融入计量经济学至关重要，但复杂模型的导出正交分数仍是一个主要挑战。我们针对广泛的情景类提供了明确的构建策略。该框架确保目标参数估计量的渐近正态性，且其实现方式不依赖于用于构建 nuisance 参数估计量的具体方法，前提是这些估计量具有 $o_p(n^{-\1/4})$ 一致性。我们将该方法应用于具有二元工具变量的因果推断，提出了一种新颖且稳健的治疗效应估计量。数值研究表明，我们的方法在有限样本中显著优于朴素替代方案。对俄勒冈健康保险实验的实证应用展示了该框架在提供稳健因果证据方面的实用性。