We provide novel bounds on average treatment effects (on the treated) that are valid under an unconfoundedness assumption. Our bounds are designed to be robust in challenging situations, for example, when the conditioning variables take on a large number of different values in the observed sample, or when the overlap condition is violated. This robustness is achieved by only using limited "pooling" of information across observations. Namely, the bounds are constructed as sample averages over functions of the observed outcomes such that the contribution of each outcome only depends on the treatment status of a limited number of observations. No information pooling across observations leads to so-called "Manski bounds", while unlimited information pooling leads to standard inverse propensity score weighting. We explore the intermediate range between these two extremes and provide corresponding inference methods. We show in Monte Carlo experiments and through two empirical application that our bounds are indeed robust and informative in practice.

翻译：本文在无混杂性假设下，提出了关于平均处理效应（或处理组平均处理效应）的新边界估计方法。所提出的边界估计在具有挑战性的情境中具有鲁棒性，例如当观测样本中协变量取值数量庞大时，或当重叠性条件不满足时。这种鲁棒性是通过仅对跨观测值信息进行有限"聚合"实现的。具体而言，边界估计被构造为观测结果函数的样本平均值，其中每个观测结果的贡献仅取决于有限数量观测值的处理状态。完全不进行信息聚合会得到所谓的"Manski边界"，而无限信息聚合则会导致标准的逆倾向得分加权估计。本文探索了这两种极端情况之间的中间范围，并提供了相应的统计推断方法。通过蒙特卡洛模拟实验和两个实证应用，我们证明了所提出的边界估计在实践中确实具有鲁棒性和信息量。