论文标题
使用仪器变量和环境来估计因果关系的因果效应
Estimating Causal Effects with Hidden Confounding using Instrumental Variables and Environments
论文作者
论文摘要
最近的工作提出了在数据收集环境中不变的回归模型。这些估计量通常在施加的环境和不变性类型的条件下具有因果解释。最近的一个例子是因果关系（CD），在隐藏的混杂状态下是一致的，代表了经典仪器变量估计量（例如两个阶段最小二乘（TSL））的替代方案。在这项工作中，我们将CD得出作为一般的矩（GMM）估计量的广义方法。 GMM表示会导致几个实际结果，包括1）创建广义因果丹齐格（GCD）估计量，可以应用于无法适应CD的连续环境问题的问题2）混合型（GCD-TSLS组合）估计器，该估计量单独使用GCD或TSL优于GCD或TSL的属性3）直接使用逐渐添加的gcmm glodotoctoctoctoction growsotic sodect gmm gmm gmm gmm。我们比较了模拟中的CD，GCD，TSL和混合估计器，并将其应用于流式细胞仪数据集。在许多情况下，新提出的GCD和混合估计器在现有方法中具有卓越的性能。
Recent works have proposed regression models which are invariant across data collection environments. These estimators often have a causal interpretation under conditions on the environments and type of invariance imposed. One recent example, the Causal Dantzig (CD), is consistent under hidden confounding and represents an alternative to classical instrumental variable estimators such as Two Stage Least Squares (TSLS). In this work we derive the CD as a generalized method of moments (GMM) estimator. The GMM representation leads to several practical results, including 1) creation of the Generalized Causal Dantzig (GCD) estimator which can be applied to problems with continuous environments where the CD cannot be fit 2) a Hybrid (GCD-TSLS combination) estimator which has properties superior to GCD or TSLS alone 3) straightforward asymptotic results for all methods using GMM theory. We compare the CD, GCD, TSLS, and Hybrid estimators in simulations and an application to a Flow Cytometry data set. The newly proposed GCD and Hybrid estimators have superior performance to existing methods in many settings.
