We introduce a flexible framework that produces high-quality almost-exact matches for causal inference. Most prior work in matching uses ad-hoc distance metrics, often leading to poor quality matches, particularly when there are irrelevant covariates. In this work, we learn an interpretable distance metric for matching, which leads to substantially higher quality matches. The learned distance metric stretches the covariate space according to each covariate's contribution to outcome prediction: this stretching means that mismatches on important covariates carry a larger penalty than mismatches on irrelevant covariates. Our ability to learn flexible distance metrics leads to matches that are interpretable and useful for the estimation of conditional average treatment effects.

翻译：我们提出了一种灵活的框架，用于生成因果推断中高质量的近似精确匹配。以往的匹配研究大多采用启发式距离度量，尤其在存在无关协变量时，常导致匹配质量低下。本研究通过学习一种可解释的距离度量进行匹配，显著提升了匹配质量。该学习得到的距离度量根据每个协变量对结果预测的贡献程度对协变量空间进行拉伸：这一拉伸机制使得重要协变量上的不匹配比无关协变量上的不匹配承担更大的惩罚。我们学习灵活距离度量的能力，使生成的匹配结果具有可解释性，并有助于估计条件平均处理效应。