Propensity score estimation with boosted regression for evaluating causal effects in observational studies
Propensity score estimation with boosted regression for evaluating causal effects in observational studies
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DOI：
10.1037/1082-989x.9.4.403
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发表时间：
2004-12-01
影响因子：
7
通讯作者：
Morral, AR
中科院分区：
文献类型：
--
作者：
McCaffrey, DF;Ridgeway, G;Morral, AR
用自然主义而不是实验数据建立因果关系模型是一个挑战。在观察性研究中，不同治疗条件下的受试者在影响结局的治疗前特征上也可能存在差异。倾向评分方法理论上可以消除所有观察到的协变量的这些混淆，但倾向评分的准确估计受到大量协变量、与治疗选择相关的不确定函数形式以及其他问题的阻碍。这篇文章表明，boosting，一个现代的统计技术，可以克服这些障碍。作者用一项对青少年缓刑犯进行药物滥用治疗的研究来说明这种方法。使用加强估计的倾向评分权重消除了大多数预处理组的差异，并大大改变了青少年物质滥用治疗的明显相对影响。
Causal effect modeling with naturalistic rather than experimental data is challenging. In observational studies participants in different treatment conditions may also differ on pretreatment characteristics that influence outcomes. Propensity score methods can theoretically eliminate these confounds for all observed covariates, but accurate estimation of propensity scores is impeded by large numbers of covariates, uncertain functional forms for their associations with treatment selection, and other problems. This article demonstrates that boosting, a modem statistical technique, can overcome many of these obstacles. The authors illustrate this approach with a study of adolescent probationers in substance abuse treatment programs. Propensity score weights estimated using boosting eliminate most pretreatment group differences and substantially alter the apparent relative effects of adolescent substance abuse treatment.