Replication data for: Multivariate Matching Methods That are Monotonic Imbalance Bounding

We introduce a new "Monotonic Imbalance Bounding" (MIB) class of matching methods for causal inference with a surprisingly large number of attractive statistical properties. MIB generalizes and extends in several new directions the only existing class, "Equal Percent Bias Reducing" (EPBR), which is designed to satisfy weaker properties and only in expectation. We also offer strategies to obtain specific members of the MIB class, and analyze in more detail a member of this class, called Coarsened
Exact Matching, whose properties we analyze from this new perspective. We offer a variety of analytical results and numerical simulations that demonstrate how members of the MIB class can dramatically improve inferences relative to EPBR-based matching methods.
<br /><br /> See also: <a href="http://gking.harvard.edu/category/research-interests/methods/causal-inference" target="_blank">Casual Inference</a>

我们提出了一类新的"单调不平衡约束"（Monotonic Imbalance Bounding，MIB）匹配方法，用于因果推断，该类方法具有数量惊人的优良统计特性。MIB从多个新方向对现有唯一类别——"等百分比偏差减少"（Equal Percent Bias Reducing，EPBR）——进行了泛化与扩展；EPBR的设计仅满足较弱的性质，且仅在期望意义下成立。我们还提供了获取MIB类中特定成员的策略，并从这一新视角更详细地分析了该类中的一个成员——粗化精确匹配（Coarsened Exact Matching）——的性质。我们提供了多种分析结果与数值模拟，以证明MIB类成员相较于基于EPBR的匹配方法，能显著提升推断效果。<br /><br />另见：<a href="http://gking.harvard.edu/category/research-interests/methods/causal-inference" target="_blank">因果推断（Casual Inference）</a>