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Using recursive partitioning to find and estimate heterogenous treatment effects in randomized clinical trials
Journal of Experimental Criminology ( IF 3.701 ) Pub Date : 2020-03-05 , DOI: 10.1007/s11292-019-09410-0
Richard Berk , Matthew Olson , Andreas Buja , Aurélie Ouss

Objectives

When for an RCT heterogeneous treatment effects are inductively obtained, significant complications are introduced. Special loss functions may be needed to find local, average treatment effects followed by techniques that properly address post-selection statistical inference.

Methods

Reanalyzing a recidivism RCT, we use a new form of classification trees to seek heterogeneous treatment effects and then correct for “data snooping” with novel inferential procedures.

Results

There are perhaps increases in recidivism for a small subset of offenders whose risk factors place them toward the right tail of the risk distribution.

Conclusions

A legitimate but partial account for uncertainty might well reject the null hypothesis of no heterogenous treatment effects. An equally legitimate but far more complete account of uncertainty for this study fails to reject the null hypothesis of no heterogeneous treatment effects.



中文翻译:

在随机临床试验中使用递归分区来发现和估计异质性治疗效果

目标

当对于 RCT 异质治疗效果归纳获得时,会引入显着的并发症。可能需要特殊的损失函数来找到局部的平均处理效果,然后是正确解决选择后统计推断的技术。

方法

重新分析累犯 RCT,我们使用一种新形式的分类树来寻找异质治疗效果,然后使用新的推理程序纠正“数据窥探”。

结果

对于一小部分罪犯,其风险因素将他们置于风险分布的右尾,可能会增加累犯率。

结论

对不确定性的合理但部分解释可能会拒绝没有异质治疗效果的零假设。本研究对不确定性的同样合理但更完整的解释未能拒绝没有异质治疗效果的零假设。

更新日期:2020-03-05
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