论文标题
通过正则校准估计，模型辅助敏感性分析对未衡量的混杂效果的治疗效果分析
Model-assisted sensitivity analysis for treatment effects under unmeasured confounding via regularized calibrated estimation
论文作者
论文摘要
考虑敏感性分析，以估计未衡量的混杂状态下的平均治疗效果，被假定满足边际灵敏度模型。在人口水平上，我们为急剧的人口界限提供了新的表示，并为Dorn，Guo和Kallus得出的双重稳健估计功能提供了双重强大的估计功能。根据加权线性结局的分位数回归，我们还得出了新的，放松的人口界限。在样本级别上，我们开发了新的方法和理论，不仅可以在倾向得分模型或结果平均回归模型的错误指定方面获得松弛人口界限的双重稳健点估计器，而且如果倾向分数正确指定了倾向分数，则在模型辅助置信区间中，而且均值分数分数和均值的回归模型可能是错误确定的。如果正确指定了结果分数回归，则松弛的种群界限会减小到急剧的边界。对于线性结果平均回归模型，置信区间也具有双重稳定性。我们的方法涉及正规化的校准估计，带有套索惩罚但精心选择的损失功能，用于拟合倾向得分和结果平均值和分位数回归模型。我们向观察性研究提出了一项模拟研究和经验应用，该研究对右心导管插入的影响。
Consider sensitivity analysis for estimating average treatment effects under unmeasured confounding, assumed to satisfy a marginal sensitivity model. At the population level, we provide new representations for the sharp population bounds and doubly robust estimating functions, recently derived by Dorn, Guo, and Kallus. We also derive new, relaxed population bounds, depending on weighted linear outcome quantile regression. At the sample level, we develop new methods and theory for obtaining not only doubly robust point estimators for the relaxed population bounds with respect to misspecification of a propensity score model or an outcome mean regression model, but also model-assisted confidence intervals which are valid if the propensity score model is correctly specified, but the outcome quantile and mean regression models may be misspecified. The relaxed population bounds reduce to the sharp bounds if outcome quantile regression is correctly specified. For a linear outcome mean regression model, the confidence intervals are also doubly robust. Our methods involve regularized calibrated estimation, with Lasso penalties but carefully chosen loss functions, for fitting propensity score and outcome mean and quantile regression models. We present a simulation study and an empirical application to an observational study on the effects of right heart catheterization.
