论文标题
有效的协变量平衡局部平均治疗效果
Efficient Covariate Balancing for the Local Average Treatment Effect
论文作者
论文摘要
本文开发了一种经验平衡方法，用于使用有条件独立的仪器变量在双向违规情况下估算治疗效果。该方法同时使用治疗和结果信息，并具有逆概率，以在跨仪器水平组之间产生精确的有限样本平衡。它没有关于结果或治疗选择步骤的功能形式假设。通过调整仪器倾向得分的损失函数，与常规的反相反概率加权方法相比，所得的治疗效果估计值既显示出低偏差和有限样本的差异。与常规的两阶段最小二乘正方形估计相比，估计器自动加权并具有相似的偏置特性，而在恒定的因果效应下，估计量的偏差性能。我们为渐近正态性和半参数效率提供条件，并演示了如何利用有关治疗选择步骤的其他信息，以减少有限样品的偏差。该方法可以轻松地与正规化或其他统计学习方法结合使用，以处理大量观察到的混杂变量。蒙特卡洛模拟表明，理论优势可以很好地转化为有限样品。该方法在经验示例中进行了说明。
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.
