An optimization approach for making causal inferences
An optimization approach for making causal inferences
复制标题
一种进行因果推断的优化方法
DOI：
10.1111/stan.12004
复制
发表时间：
2013
影响因子：
1.5
通讯作者：
E. Sewell
中科院分区：
文献类型：
--
作者：
Wendy K. Tam Cho;Jason J. Sauppe;Alexander G. Nikolaev;S. Jacobson;E. Sewell
为了从观测数据中做出因果推断，研究人员经常求助于匹配方法。这些方法的成功程度各不相同。我们通过将匹配问题重新定义为子集选择问题来解决匹配方法的问题。给定一组协变量，我们寻求找到两个子集，一个控制组和一个处理组，以便我们获得最优平衡，或者换句话说，这些协变量在控制组和处理组中的分布之间的最小差异。我们的公式捕获了Rubin因果模型的关键元素，并很好地转换为离散优化框架。
To make causal inferences from observational data, researchers have often turned to matching methods. These methods are variably successful. We address issues with matching methods by redefining the matching problem as a subset selection problem. Given a set of covariates, we seek to find two subsets, a control group and a treatment group, so that we obtain optimal balance, or, in other words, the minimum discrepancy between the distributions of these covariates in the control and treatment groups. Our formulation captures the key elements of the Rubin causal model and translates nicely into a discrete optimization framework.