To further develop the statistical inference problem for heterogeneous treatment effects, this paper builds on Breiman's (2001) random forest tree (RFT)and Wager et al.'s (2018) causal tree to parameterize the nonparametric problem using the excellent statistical properties of classical OLS and the division of local linear intervals based on covariate quantile points, while preserving the random forest trees with the advantages of constructible confidence intervals and asymptotic normality properties [Athey and Imbens (2016),Efron (2014),Wager et al.(2014)\citep{wager2014asymptotic}], we propose a decision tree using quantile classification according to fixed rules combined with polynomial estimation of local samples, which we call the quantile local linear causal tree (QLPRT) and forest (QLPRF).
翻译:为进一步发展不同处理效果的统计推断问题,本文件以Breiman(2001年)随机森林树(RFT)和Wager等人(2018年)的因果树为基础,利用古典OLS的极佳统计特性和根据共变量点对当地线性间隔的划分,将非参数问题参数化,同时保留随机森林树木,其优点是可建构信任间隔和无药可循的正常特性[AYES和Imbens ⁇ 、Efron(2014)、Wager等人(2014年)\citep{wager2014-masymptography}],我们提议根据固定规则加上对当地样品的多分子估计,采用定量分类法,即我们称之为四分本地线性线性树(QLPRF)和森林(QLPRF)。