We propose an improved estimator of the complier average causal effect (CACE). Researchers typically choose a presumably consistent estimator for CACE in studies with noncompliance when many other lower variance estimators may be available. We propose a synthetic estimator that combines information across all available estimators, leveraging the efficiency in lower variance estimators while maintaining low bias. Our approach minimizes an estimate of the mean squared error of all convex combinations of the candidate estimators. We derive the asymptotic distribution of the synthetic estimator and demonstrate its good performance in simulation, displaying robustness to inclusion of even high-bias estimators.

中文翻译：

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编译器平均因果效应的综合估计

我们提出了一种改进的编译器平均因果效应 (CACE) 估计量。当可能有许多其他较低方差的估计量可用时，研究人员通常会在不合规的研究中为 CACE 选择一个可能一致的估计量。我们提出了一种综合估计器，它结合了所有可用估计器的信息，利用低方差估计器的效率，同时保持低偏差。我们的方法最小化了候选估计量的所有凸组合的均方误差估计。我们推导了合成估计量的渐近分布，并证明了它在模拟中的良好性能，即使包含高偏差估计量也表现出鲁棒性。