摘要翻译：
本文提出了一种经验平衡方法来估计治疗效果的双边不依从性使用二元条件独立的工具变量。该方法用反向概率来权衡治疗和结果信息，以产生精确的跨仪器水平组的有限样本平衡。它没有对结果或治疗选择步骤的功能形式假设。通过对仪器倾向性分数的损失函数进行裁剪，与传统的逆概率加权方法相比，所得到的治疗效果估计在有限样本中表现出低偏差和减少的方差。该估计器是自动权重归一化的，与常规的两级最小二乘估计相比，具有相似的偏差性质。我们给出了渐近正态性和半参数有效性的条件，并证明了如何利用有限样本中关于处理选择步骤的附加信息来减少偏差。该方法可以很容易地与正则化或其他统计学习方法相结合，以处理观察到的高维数的混杂变量。蒙特卡罗模拟表明，理论上的优势很好地适用于有限样本。通过一个经验实例说明了该方法。
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英文标题：
《Efficient Covariate Balancing for the Local Average Treatment Effect》
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作者：
Phillip Heiler
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最新提交年份：
2020
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
  This paper develops an empirical balancing approach for the estimation of treatment effects under two-sided noncompliance using a binary conditionally independent instrumental variable. The method weighs both treatment and outcome information with inverse probabilities to produce exact finite sample balance across instrument level groups. It is free of functional form assumptions on the outcome or the treatment selection step. By tailoring the loss function for the instrument propensity scores, the resulting treatment effect estimates exhibit both low bias and a reduced variance in finite samples compared to conventional inverse probability weighting methods. The estimator is automatically weight normalized and has similar bias properties compared to conventional two-stage least squares estimation under constant causal effects for the compliers. We provide conditions for asymptotic normality and semiparametric efficiency and demonstrate how to utilize additional information about the treatment selection step for bias reduction in finite samples. The method can be easily combined with regularization or other statistical learning approaches to deal with a high-dimensional number of observed confounding variables. Monte Carlo simulations suggest that the theoretical advantages translate well to finite samples. The method is illustrated in an empirical example.
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PDF链接：
https://arxiv.org/pdf/2007.04346