To conduct causal inference in observational settings, researchers must rely on certain identifying assumptions. In practice, these assumptions are unlikely to hold exactly. This paper considers the bias of selection-on-observables, instrumental variables, and proximal inference estimates under violations of their identifying assumptions. We develop bias expressions for IV and proximal inference that show how violations of their respective assumptions are amplified by any unmeasured confounding in the outcome variable. We propose a set of sensitivity tools that quantify the sensitivity of different identification strategies, and an augmented bias contour plot visualizes the relationship between these strategies. We argue that the act of choosing an identification strategy implicitly expresses a belief about the degree of violations that must be present in alternative identification strategies. Even when researchers intend to conduct an IV or proximal analysis, a sensitivity analysis comparing different identification strategies can help to better understand the implications of each set of assumptions. Throughout, we compare the different approaches on a re-analysis of the impact of state surveillance on the incidence of protest in Communist Poland.

翻译：在观测性研究中进行因果推断时，研究者必须依赖某些识别假设。但在实践中，这些假设很难完全成立。本文考虑在违反识别假设条件下，基于可观测变量选择、工具变量以及近端推断估计的偏差问题。我们推导出工具变量和近端推断的偏差表达式，揭示了各自假设的违反如何被结果变量中未测量的混杂因素所放大。我们提出一套敏感性分析工具，用于量化不同识别策略的敏感性，并引入增强型偏差等高线图以直观展示各策略间的关系。我们认为，选择某种识别策略的举动本身隐含着对其他替代策略中必须存在的违反程度的信念。即便研究者有意采用工具变量或近端分析方法，对比不同识别策略的敏感性分析也有助于更深入理解每组假设的潜在影响。本文通过重新分析共产主义波兰时期国家监控对抗议事件发生率的影响，对上述不同方法进行了系统比较。