Observational studies are valuable tools for inferring causal effects in the absence of controlled experiments. However, these studies may be biased due to the presence of some relevant, unmeasured set of covariates. The design of an observational study has a prominent effect on its sensitivity to hidden biases, and the best design may not be apparent without examining the data. One approach to facilitate a data-inspired design is to split the sample into a planning sample for choosing the design and an analysis sample for making inferences. We devise a powerful and flexible method for selecting outcomes in the planning sample when an unknown number of outcomes are affected by the treatment. We investigate the theoretical properties of our method and conduct extensive simulations that demonstrate pronounced benefits, especially at higher levels of allowance for unmeasured confounding. Finally, we demonstrate our method in an observational study of the multi-dimensional impacts of a devastating flood in Bangladesh.

翻译：观察性研究是在缺乏受控实验的情况下推断因果效应的宝贵工具。然而，由于存在某些相关但未测量的协变量，这些研究可能存在偏差。观察性研究的设计对其对隐藏偏差的敏感性具有显著影响，而在未检查数据的情况下，最佳设计可能并不明显。促进数据启发式设计的一种方法是将样本分割为用于选择设计的规划样本和用于进行推断的分析样本。我们设计了一种强大而灵活的方法，用于在未知数量的结果受处理影响时，在规划样本中选择结果。我们研究了该方法的理论特性，并进行了广泛的模拟，结果表明该方法具有显著优势，尤其是在对未测量混杂因素的允许程度较高时。最后，我们在对孟加拉国一场毁灭性洪水的多维影响进行观察性研究中展示了我们的方法。