Quantile treatment effect estimation with dimension reduction
Quantile treatment effect estimation with dimension reduction
复制标题
降维的分位数处理效果估计
DOI：
10.1080/24754269.2019.1696645
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发表时间：
2020-07
影响因子：
0.5
通讯作者：
Jun Shao
中科院分区：
文献类型：
--
作者：
Ying Zhang;Lei Wang;Menggang Yu;Jun Shao
Quantile treatment effects can be important causal estimands in evaluation of biomedical treatments or interventions for health outcomes such as medical cost and utilisation. We consider their estimation in observational studies with many possible covariates under the assumption that treatment and potential outcomes are independent conditional on all covariates. To obtain valid and efficient treatment effect estimators, we replace the set of all covariates by lower dimensional sets for estimation of the quantiles of potential outcomes. These lower dimensional sets are obtained using sufficient dimension reduction tools and are outcome specific. We justify our choice from efficiency point of view. We prove the asymptotic normality of our estimators and our theory is complemented by some simulation results and an application to data from the University of Wisconsin Health Accountable Care Organization.
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