自动本地鲁棒GMM与机器学习生成的回归器
Automatic Locally Robust GMM with Machine-Learning-Generated Regressors
AI总结：
本文提出一种三步本地鲁棒GMM框架，用于处理机器学习生成的回归器，通过下游局部鲁棒性消除间接影响，减少偏误。
摘要：
机器学习（ML）方法现在经常生成用于后续计量分析的回归器，例如估计的倾向分数、控制函数残差、插补的协变量、学习的代理或高维数据的低维嵌入。随着这些ML生成的回归器变得普遍，缺乏一般推断方法已成为关键限制。标准的插件和双ML程序忽略了生成回归器如何进入后续阶段，导致大偏误和无效推断。我们开发了一种三步本地鲁棒GMM框架用于推断ML生成的回归器。关键的新见解是下游局部鲁棒性：通过函数链式法则，构造的矩函数与第二步正交，消除了ML生成回归器的复杂间接（条件）影响。我们展示如何通过交叉拟合的辅助回归估计相关Riesz代表者来实现这一点，允许在早期步骤中使用通用非Donsker ML。在领先处理效应和反事实设置中，模拟显示现有方法存在严重偏误，而使用我们的程序可减少85-95%的偏误。
英文摘要：
Machine-learning (ML) methods now routinely generate regressors used in subsequent econometric analyses, for example, estimated propensity scores, control-function residuals, imputed covariates, learned proxies, or low-dimensional embeddings of high-dimensional data. As these ML-generated regressors become ubiquitous, the lack of general inference methods for models that use them has become a critical limitation. Standard plug-in and Double ML procedures ignore how generated regressors enter later stages, leading to large biases and invalid inference. We develop a three-step locally robust GMM framework for inference with ML generated regressors. A key new insight is downstream local robustness: by a functional chain rule, moment functions that are constructed to be orthogonal to the second step eliminate the complicated indirect (conditioning) effects from the ML-generated regressors. We show how to implement this automatically by estimating the associated Riesz representers through cross-fitted auxiliary regressions, allowing for generic non-Donsker ML in both early steps. In leading treatment-effect and counterfactual settings, simulations demonstrate severe bias in existing methods and reductions of 85-95% using our procedures.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
