Adversarial Estimation of Riesz Representers
Adversarial Estimation of Riesz Representers
复制标题
Riesz 代表的对抗性估计
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Vasilis Syrgkanis
中科院分区：
文献类型：
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作者：
V. Chernozhukov;Whitney Newey;Rahul Singh;Vasilis Syrgkanis
许多因果参数是基础回归的线性函数。 Riesz 表示器是半参数估计线性函数渐近方差的关键组成部分。我们提出了一个对抗性框架来使用通用函数空间来估计 Riesz 表示器。我们用称为临界半径的抽象量证明了非渐近均方率，然后将其专门用于神经网络、随机森林和再现核希尔伯特空间作为主要案例。我们的估计器与具有样本分割功能的有针对性、无偏差的机器学习高度兼容；我们的保证直接验证允许错误指定的一般推理条件。我们还根据稳定性或复杂性，使用我们的保证来证明推理，而无需样本分割。我们的估计器在高度非线性模拟中实现了名义覆盖，而以前的一些方法却失效了。他们为匹配赠款的异质效应提供了新的视角。
Many causal parameters are linear functionals of an underlying regression. The Riesz representer is a key component in the asymptotic variance of a semiparametrically estimated linear functional. We propose an adversarial framework to estimate the Riesz representer using general function spaces. We prove a nonasymptotic mean square rate in terms of an abstract quantity called the critical radius, then specialize it for neural networks, random forests, and reproducing kernel Hilbert spaces as leading cases. Our estimators are highly compatible with targeted and debiased machine learning with sample splitting; our guarantees directly verify general conditions for inference that allow mis-specification. We also use our guarantees to prove inference without sample splitting, based on stability or complexity. Our estimators achieve nominal coverage in highly nonlinear simulations where some previous methods break down. They shed new light on the heterogeneous effects of matching grants.