We propose a novel sensitivity analysis framework for linear estimands when identification failure can be viewed as seeing the wrong distribution of outcomes. Our family of assumptions bounds the density ratio between the observed and true conditional outcome distribution. This framework links naturally to selection models, generalizes existing assumptions for the Regression Discontinuity (RD) and Inverse Propensity Weighting (IPW) estimand, and provides a novel nonparametric perspective on violations of identification assumptions for ordinary least squares (OLS). Our sharp partial identification results extend existing results for IPW to cover other estimands and assumptions that allow even unbounded likelihood ratios, yielding a simple and unified characterization of bounds under assumptions like c-dependence of Masten and Poirier (2018). The sharp bounds can be written as a simple closed form moment of the data, the nuisance functions estimated in the primary analysis, and the conditional outcome quantile function. We find our method does well in simulations even when targeting a discontinuous and nearly infinite bound.

翻译：我们提出了一种针对线性估计量的新型灵敏度分析框架，适用于识别失败可被视为观测到错误结果分布的情形。该框架通过假设对观测条件结果分布与真实条件结果分布之间的密度比施加约束，自然衔接选择模型，泛化了回归间断设计（RD）和逆概率加权（IPW）估计量的现有假设，并为普通最小二乘法（OLS）识别假设的违背问题提供了全新的非参数视角。我们的精确部分识别结果将IPW已有结论推广至其他估计量及允许无界似然比的假设，从而基于Masten与Poirier（2018）提出的c-依赖性等假设，得到简洁统一的边界刻画特征。这些精确边界可表示为数据的简单闭式矩、主分析中估计的干扰函数及条件结果分位函数的组合。仿真实验表明，即便在目标边界不连续且近乎无界的情况下，该方法仍表现良好。