论文标题
因果模拟实验：偏置扩增的教训
Causal Simulation Experiments: Lessons from Bias Amplification
论文作者
论文摘要
因果推论的最新理论工作探索了一类重要的变量，在遵循的条件下，该变量可能会进一步扩大现有的未衡量的混杂偏见（偏差放大）。 Despite this theoretical work, existing simulations of bias amplification in clinical settings have suggested bias amplification may not be as important in many practical cases as suggested in the theoretical literature.We resolve this tension by using tools from the semi-parametric regression literature leading to a general characterization in terms of the geometry of OLS estimators which allows us to extend current results to a larger class of DAGs, functional forms, and distributional assumptions.我们进一步使用这些结果来了解当前模拟方法的局限性，并提出一个新的框架，以进行因果模拟实验以比较估计器。然后，我们评估将这种仿真方法扩展到具有二元处理的真实临床数据集的背景下的挑战和好处，为有原则的方法奠定了基础，以实现敏感性分析，以实现未衡量的混淆。
Recent theoretical work in causal inference has explored an important class of variables which, when conditioned on, may further amplify existing unmeasured confounding bias (bias amplification). Despite this theoretical work, existing simulations of bias amplification in clinical settings have suggested bias amplification may not be as important in many practical cases as suggested in the theoretical literature.We resolve this tension by using tools from the semi-parametric regression literature leading to a general characterization in terms of the geometry of OLS estimators which allows us to extend current results to a larger class of DAGs, functional forms, and distributional assumptions. We further use these results to understand the limitations of current simulation approaches and to propose a new framework for performing causal simulation experiments to compare estimators. We then evaluate the challenges and benefits of extending this simulation approach to the context of a real clinical data set with a binary treatment, laying the groundwork for a principled approach to sensitivity analysis for bias amplification in the presence of unmeasured confounding.
