论文标题
违反近端识别假设的偏见公式
Bias Formulas for Violations of Proximal Identification Assumptions
论文作者
论文摘要
观察数据的因果推论通常取决于没有无法衡量的混杂的无法验证的假设。最近，Tchetgen Tchetgen及其同事引入了近端推断，以利用负面控制结果和暴露，作为代理，以调整未衡量的混杂因素。但是，近端推理所依赖的一些关键假设本身是无法检验的。此外，尚不清楚侵犯近端推理假设对效应估计偏差的影响。在本文中，我们在线性结构方程模型数据生成过程下得出了近端推理估计量的偏置公式。这些结果是对近端推理估计量的灵敏度分析和定量偏差分析的第一步。虽然仅限于特定的数据生成过程，但我们的结果可能会对近端推理估计量的行为提供更多的一般见解。
Causal inference from observational data often rests on the unverifiable assumption of no unmeasured confounding. Recently, Tchetgen Tchetgen and colleagues have introduced proximal inference to leverage negative control outcomes and exposures as proxies to adjust for bias from unmeasured confounding. However, some of the key assumptions that proximal inference relies on are themselves empirically untestable. Additionally, the impact of violations of proximal inference assumptions on the bias of effect estimates is not well understood. In this paper, we derive bias formulas for proximal inference estimators under a linear structural equation model data generating process. These results are a first step toward sensitivity analysis and quantitative bias analysis of proximal inference estimators. While limited to a particular family of data generating processes, our results may offer some more general insight into the behavior of proximal inference estimators.
