发表机构
School of Economics, University of Sydney（悉尼大学经济系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究在机器学习工具变量估计中，构建异质性稳健正交得分，恢复对固定目标的√N推断，提供豪斯曼型诊断和识别稳健置信集，解决通常去偏矩非奈曼正交及朴素推断仅对依赖学习者目标有效的问题。
AI 中文摘要
工具变量估计越来越多地将许多或高维工具汇集到一个机器学习的第一阶段，并剔除丰富的控制变量。由此产生的估计量，即从工具的任何信号构建的剔除控制变量后的IV系数，是异质性效应的信号加权平均值，这使得不透明的第一阶段具有精确的结构意义。当协方差单调性条件成立时，该平均值是凸的，我们基于向量单调性为该条件提供了微观基础。然而，对于一个学习到的信号，通常的去偏矩不是奈曼正交的，其一阶偏差是朝着学习者自身信号加权平均值的漂移，因此朴素推断仅对该依赖学习者的目标有效。我们构建了一个异质性稳健的正交得分，在不损失效率的情况下恢复对固定的、与学习者无关的目标的√N推断，并提供了一个豪斯曼型诊断和识别稳健的置信集。
英文摘要
Instrumental-variables estimation increasingly pools many or high-dimensional instruments into a single machine-learned first stage, with rich controls partialled out. The resulting estimand, the partialled-out IV coefficient built from any signal of the instruments, is a signal-weighted average of the heterogeneous effects, which gives an opaque first stage a precise structural meaning. The average is convex whenever a covariance-monotonicity condition holds, and we provide a microfoundation for that condition based on vector monotonicity. With a learned signal, however, the usual debiased moment is not Neyman-orthogonal, and its first-order bias is a drift toward the learner's own signal-weighted average, so naive inference remains valid only for that learner-dependent target. We construct a heterogeneity-robust orthogonal score that restores $\sqrt{N}$ inference on the fixed, learner-invariant target at no efficiency cost, and provide a Hausman-type diagnostic and identification-robust confidence sets.
