论文标题
K级估计器和脉冲的分布鲁棒性
Distributional robustness of K-class estimators and the PULSE
论文作者
论文摘要
尽管因果模型是强大的，因为它们在任意强大的干预措施下是最佳的预测，但是当干预措施界定时，它们可能不是最佳的。我们证明，经典的K级估计器通过建立K级估计器和锚回归之间的联系来满足这种最优性。这种连接进一步激发了仪器变量设置中的新型估计器，该估计值将估计器位于因果系数的渐近有效置信区域的约束中最小化了平方平方的预测误差。我们称此估计器脉冲（p-无关的最小二乘估计器）将其与不变性相关联，表明它可以作为数据驱动的K级估计器有效地计算，即使基本优化问题是非convex，并且证明了一致性。我们评估了实际数据的估计器，并执行模拟实验，说明脉搏的变异性较小。有几种设置，包括较弱的仪器设置，它胜过其他估计器。
While causal models are robust in that they are prediction optimal under arbitrarily strong interventions, they may not be optimal when the interventions are bounded. We prove that the classical K-class estimator satisfies such optimality by establishing a connection between K-class estimators and anchor regression. This connection further motivates a novel estimator in instrumental variable settings that minimizes the mean squared prediction error subject to the constraint that the estimator lies in an asymptotically valid confidence region of the causal coefficient. We call this estimator PULSE (p-uncorrelated least squares estimator), relate it to work on invariance, show that it can be computed efficiently as a data-driven K-class estimator, even though the underlying optimization problem is non-convex, and prove consistency. We evaluate the estimators on real data and perform simulation experiments illustrating that PULSE suffers from less variability. There are several settings including weak instrument settings, where it outperforms other estimators.
