Replication Data for: Looking for twins: how to build better counterfactuals with matching

A primary challenge for researchers that make use of observational data is selection bias (i.e., the units of analysis exhibit systematic differences and dis-homogeneities due to non-random selection into treatment). This article encourages researchers in acknowledging this problem and discusses how and - more importantly - under which assumptions they may resort to statistical matching techniques to reduce the imbalance in the empirical distribution of pre-treatment observable variables between the treatment and control groups. With the aim of providing a practical guidance, the article engages with the evaluation of the effectiveness of peacekeeping missions in the case of the Bosnian civil war, a research topic in which selection bias is a structural feature of the observational data researchers have to use, and shows how to apply the Coarsened Exact Matching (CEM), the most widely used matching algorithm in the fields of Political Science and International Relations.利用观测数据开展研究的研究者所面临的核心挑战之一是选择偏倚（selection bias），即分析单元因非随机被分配至处理组，而呈现出系统性差异与非均质性。本文呼吁研究者正视该问题，并探讨了研究者可如何借助统计匹配技术——更关键的是，需在何种假设条件下——以缩减处理组与对照组间预处理可观测变量经验分布的失衡状况。为提供实操性指导，本文围绕波斯尼亚内战情境下的维和行动效能评估展开探讨，该主题中选择偏倚是研究者不得不使用的观测数据的结构性特征；文中还演示了粗化精确匹配（Coarsened Exact Matching，CEM）的应用方法，该算法是政治学与国际关系领域应用最为广泛的匹配算法。