Causal Diagrams for Treatment Effect Estimation with Application to Efficient Covariate Selection
展示了如何利用机器学习中的因果图方法，详细分析高效协变量的选择，帮助经济学家更严谨地设计处理效应估计量。
Careful examination of the structure determining treatment choice and outcomes, as advocated by Heckman (2008), is central to the design of treatment effect estimators and, in particular, proper choice of covariates. Here, we demonstrate how causal diagrams developed in the machine learning literature by Judea Pearl and his colleagues, but not so well known to economists, can play a key role in this examination by using these methods to give a detailed analysis of the choice of efficient covariates identified by Hahn (2004). © 2011 The President and Fellows of Harvard College and the Massachusetts Institute of Technology.