Instrumental variables (IVs) are widely used for estimating causal effects in the presence of unmeasured confounding. Under the standard IV model, however, the average treatment effect (ATE) is only partially identifiable. To address this, we propose novel assumptions that allow for identification of the ATE. Our identification assumptions are clearly separated from model assumptions needed for estimation, so that researchers are not required to commit to a specific observed data model in establishing identification. We then construct multiple estimators that are consistent under three different observed data models, and multiply robust estimators that are consistent in the union of these observed data models. We pay special attention to the case of binary outcomes, for which we obtain bounded estimators of the ATE that are guaranteed to lie between -1 and 1. Our approaches are illustrated with simulations and a data analysis evaluating the causal effect of education on earnings.

中文翻译：

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使用工具变量对平均治疗效果进行有界，有效和多重稳健的估计。

工具变量（IVs）被广泛用于在存在无法衡量的混杂因素的情况下估计因果效应。但是，在标准IV模型下，平均治疗效果（ATE）只能部分确定。为了解决这个问题，我们提出了可以识别ATE的新颖假设。我们的识别假设与估计所需的模型假设明显分开，因此研究人员在建立识别过程中无需遵循特定的观察数据模型。然后，我们构造在三个不同的观测数据模型下一致的多个估计量，并乘以在这些观测数据模型的并集中一致的鲁棒估计量。我们特别注意二进制结果的情况，