最优连续处理效应的识别与估计

中文摘要 AI 辅助

估计连续处理效应是困难的，因为平均导数估计量依赖于一个不适定的条件密度得分。最近的研究将有界结果权重作为原始量，刻画了一类无需密度估计的加权平均导数效应。在本文中，我们发展了在同方差和异方差下该类估计量的最优有效估计目标的识别与估计理论。在识别方面，我们表明这些估计目标放宽了标准条件，在尖锐边界和内部处理荒漠处仍然有效，而经典理论的严格重叠和密度光滑性条件排除了这些情况。在估计方面，我们推导了有效影响函数，并开发了去偏机器学习估计器。

英文摘要

Estimating continuous treatment effects is hard because average-derivative estimators rely on an ill-posed conditional-density score. Recent work makes a bounded outcome weight the primitive, characterizing a class of weighted average derivative effects without density estimation. In this paper, we develop the identification and estimation theory for the optimally efficient estimands of this class under homoskedasticity and heteroskedasticity. On identification, we show that these estimands relax standard conditions, remaining valid at sharp boundaries and at interior treatment deserts that the strict overlap and density-smoothness conditions of classical theory rule out. On estimation, we derive the efficient influence function, and develop Debiased Machine Learning estimators.

发表机构

• School of Economics, University of Sydney（悉尼大学经济系）

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