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Partial identification of the treatment effect distribution and its functionals

机译:局部识别治疗效果分布及其功能

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In the treatment effect problem, the available information is on the marginal distributions of potential outcomes, but not on their joint distribution. The only point identified functional of the treatment effect distribution is its average, the average treatment effect (ATE). Quantiles and other functionals of the distribution of treatment effect are only partially identified. Bounds on a single quantile and on the cumulative distribution function (c.d.f.) in a single point are sharp (Makarov bounds). We show that bounds on functionals of the quantile process that use Makarov bounds are not sharp, because the Makarov bounds are pointwise, but not uniformly sharp. This allows us to propose improved bounds on functionals of the c.d.f. As an intermediate result, we find that the Makarov bounds on the region that contains the c.d.f. of the treatment effect distribution in a finite number of points can be improved. We provide numerical illustrations throughout the paper permitting a clear visualization of how the method works. (C) 2019 Elsevier B.V. All rights reserved.
机译:在治疗效果问题中,可用信息处于潜在结果的边际分布,但不是它们的联合分布。唯一鉴定的治疗效果分布功能是其平均值,平均治疗效果(吃)。分量和其他功能的处理效果分布仅部分识别。单个分位数和累积分布函数(C.D.F.)的界限是夏普(Makarov边界)。我们展示了使用Makarov边界的分位数过程的功能的界限不是敏锐的,因为Makarov边界是令人省点的,但不均匀尖锐。这使我们能够在C.D.F的功能上提出改进的界限。作为中间结果,我们发现Makarov边界包含C.d.f.可以提高有限数量点中的治疗效果分布。我们在整个论文中提供了数值插图,允许清楚可视化方法如何工作方式。 (c)2019年Elsevier B.V.保留所有权利。

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