Matching promises transparent causal inferences for observational data, making it an intuitive approach for many applications. In practice, however, standard matching methods often perform poorly compared to modern approaches such as response-surface modeling and optimizing balancing weights. We propose Caliper Synthetic Matching (CSM) to address these challenges while preserving simple and transparent matches and match diagnostics. CSM extends Coarsened Exact Matching by incorporating general distance metrics, adaptive calipers, and locally constructed synthetic controls. We show that CSM can be viewed as a monotonic imbalance bounding matching method, so that it inherits the usual bounds on imbalance and bias enjoyed by MIB methods. We further provide a bound on a measure of joint covariate imbalance. Using a simulation study, we illustrate how CSM can even outperform modern matching methods in certain settings, and finally illustrate its use in an empirical example. Overall, we find CSM allows for many of the benefits of matching while avoiding some of the costs.

翻译：匹配方法为观测数据提供了透明的因果推断途径，使其成为众多应用场景中直观的研究方法。然而在实践中，标准匹配方法的表现往往逊色于响应曲面建模和优化平衡权重等现代方法。本文提出的卡尺合成匹配（CSM）在保持匹配过程与诊断的简洁性和透明性的同时，有效应对了这些挑战。CSM通过整合通用距离度量、自适应卡尺和局部构建的合成控制，对粗化精确匹配进行了扩展。我们证明CSM可视为单调不平衡边界匹配方法，因此继承了该类方法在协变量不平衡与偏差方面的理论边界。我们进一步给出了联合协变量不平衡度量的边界约束。通过模拟研究，我们展示了CSM在特定场景下甚至能超越现代匹配方法的性能，最后通过实证案例说明了其应用价值。总体而言，CSM在保留匹配方法诸多优势的同时，有效规避了部分传统缺陷。