An Application of Matching After Learning To Stretch (MALTS) to the ACIC 2018 Causal Inference Challenge Data
An Application of Matching After Learning To Stretch (MALTS) to the ACIC 2018 Causal Inference Challenge Data
复制标题
学习拉伸后匹配 (MALTS) 在 ACIC 2018 因果推理挑战赛数据中的应用
DOI：
--
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
A. Volfovsky
中科院分区：
文献类型：
--
作者：
Harsh Parikh;C. Rudin;A. Volfovsky
摘要：在因果推理的学习匹配框架中，在保留训练集上训练参数化距离度量，以便匹配产生准确的估计条件平均治疗效果。这样，匹配就可以像其他用于因果推理的黑盒机器学习技术一样准确。我们使用一种名为 Matching-After-Learning-To-Stretch (MALTS)（Parikh 等人，2018）的新学习匹配算法来研究来自大西洋因果推理挑战赛的观测数据集。除了提供（条件）平均治疗效果的估计之外，MALTS 程序还允许从业者直接评估匹配组，了解哪里可能需要收集更多数据，并了解何时可以信任估计。1
Abstract:In the learning-to-match framework for causal inference, a parameterized distance metric is trained on a holdout train set so that the matching yields accurate estimated conditional average treatment effects. This way, the matching can be as accurate as other black box machine learning techniques for causal inference. We use a new learning-to-match algorithm called Matching-After-Learning-To-Stretch (MALTS) (Parikh et al., 2018) to study an observational dataset from the Atlantic Causal Inference Challenge. Other than providing estimates for (conditional) average treatment effects, the MALTS procedure allows practitioners to evaluate matched groups directly, understand where more data might need to be collected and gain an understanding of when estimates can be trusted.1