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：我们针对在无混淆假设下有效的平均处理效应（处理组平均处理效应）提出了新的边界。这些边界旨在对具有挑战性情景保持稳健性，例如当条件变量在观测样本中取大量不同值，或重叠条件被违反时。这种稳健性通过仅利用跨观测值的有限"信息合并"实现。具体而言，边界被构造为观测结果函数的样本均值，使得每个结果的贡献仅取决于有限数量的观测值的处理状态。无跨观测值信息合并导致所谓的"Manski边界"，而无限信息合并则导致标准逆倾向得分加权。我们探究了这两种极端情况之间的中间范围，并提供了相应的推断方法。通过蒙特卡洛实验和两项实证应用，我们证明这些边界在实践中确实具有稳健性和信息性。