Background:

Random design experiments are a powerful device for estimating average treatment effects, but evaluators sometimes seek to estimate the distribution of treatment effects. For example, an evaluator might seek to learn the proportion of treated units who benefit from treatment, the proportion who receive no benefit, and the proportion who are harmed by treatment.

Method:

Imbens and Rubin (I&R) recommend a Bayesian approach to drawing inferences about the distribution of treatment effects. Drawing on the I&R recommendations, this article explains the approach; provides computing algorithms for continuous, binary, ordered and countable outcomes; and offers simulated and real-world illustrations.

Results:

This article shows how the I&R approach leads to bounded uncertainty intervals for summary measures of the distribution of treatment effects. It clarifies the nature of those bounds and shows that they are typically informative.

Conclusions:

Despite identification issues, bounded solutions provide useful insight into the distribution of treatment effects. We recommend that evaluators incorporate analyses of the distribution of treatment effects into new studies and that evaluators revisit completed studies to estimate the distribution of treatment effects.

中文翻译：

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从随机设计实验估计处理效果的分布。

背景：

随机设计实验是估计平均治疗效果的强大工具，但评估者有时会试图估计治疗效果的分布。例如，评估者可能会寻求了解从治疗中受益的治疗单位的比例、未受益的比例以及因治疗而受到伤害的比例。

方法：

Imbens 和 Rubin (I&R) 推荐使用贝叶斯方法来推断治疗效果的分布。根据 I&R 建议，本文解释了该方法；提供连续、二进制、有序和可数结果的计算算法；并提供模拟和真实世界的插图。

结果：

本文展示了 I&R 方法如何为处理效果分布的汇总度量得出有界不确定性区间。它阐明了这些界限的性质，并表明它们通常具有信息量。

结论：

尽管存在识别问题，但有界解决方案提供了对治疗效果分布的有用洞察。我们建议评估人员将治疗效果分布的分析纳入新研究，并建议评估人员重新审视已完成的研究以估计治疗效果的分布。