标题： 柔性敏感性分析在无观测含义的观察性研究中的应用 显示英文标题

标题： Flexible sensitivity analysis for observational studies without observable implications

摘要： 观察性因果推理中的一个基本挑战是，关于无混淆性的假设无法从数据中验证。 因此，在实践中评估这些假设的敏感性非常重要。 不幸的是，一些现有的敏感性分析方法无意中施加了与现代因果推理方法相冲突的限制，而现代因果推理方法强调对观测数据的灵活模型。 为了解决这个问题，我们提出了一种框架，该框架允许（1）对观测数据使用灵活模型，以及（2）识别部分和未识别部分之间的清晰分离。 我们的框架扩展了缺失数据文献中的一个方法，即Tukey因子化方法，将其应用于因果推理场景。 在这种因子化下，我们可以用未识别的选择函数表示未观察到的潜在结果分布，这些函数假设治疗分配指示符与观察到的潜在结果之间存在未识别的关系。 这个框架中的敏感性参数易于解释，我们还提供了针对可观察量校准这些参数的启发式方法。 我们在两个例子中展示了这种方法的灵活性，在这两个例子中，我们使用贝叶斯非参数模型来估计平均处理效应和分位数处理效应。 显示英文摘要

摘要： A fundamental challenge in observational causal inference is that assumptions about unconfoundedness are not testable from data. Assessing sensitivity to such assumptions is therefore important in practice. Unfortunately, some existing sensitivity analysis approaches inadvertently impose restrictions that are at odds with modern causal inference methods, which emphasize flexible models for observed data. To address this issue, we propose a framework that allows (1) flexible models for the observed data and (2) clean separation of the identified and unidentified parts of the sensitivity model. Our framework extends an approach from the missing data literature, known as Tukey's factorization, to the causal inference setting. Under this factorization, we can represent the distributions of unobserved potential outcomes in terms of unidentified selection functions that posit an unidentified relationship between the treatment assignment indicator and the observed potential outcomes. The sensitivity parameters in this framework are easily interpreted, and we provide heuristics for calibrating these parameters against observable quantities. We demonstrate the flexibility of this approach in two examples, where we estimate both average treatment effects and quantile treatment effects using Bayesian nonparametric models for the observed data.