论文标题
动态治疗效果和一般嵌套功能的自动辩护机器学习
Automatic Debiased Machine Learning for Dynamic Treatment Effects and General Nested Functionals
论文作者
论文摘要
我们将自动辩护的机器学习的想法扩展到动态治疗方案，更普遍地扩展到嵌套功能。我们表明，可以根据递归riesz的代表表征嵌套的平均回归的表征来重新说明动态处理状态的多重稳健公式。然后，我们应用了递归的Riesz代表估计学习算法，该学习算法可以估算偏低的校正，而无需表征校正项的外观，例如，在动态式方面的双重稳健估计中所做的那样，例如反概率加权项的产物。我们的方法定义了一系列损失最小化问题的顺序，其最小化是偏向校正的误解器，因此规避了解决辅助倾向模型的需求，并直接优化了目标降低偏见校正的平均平方误差。我们为动态离散选择模型的估计以及对替代物的长期影响的估计提供了进一步的应用。
We extend the idea of automated debiased machine learning to the dynamic treatment regime and more generally to nested functionals. We show that the multiply robust formula for the dynamic treatment regime with discrete treatments can be re-stated in terms of a recursive Riesz representer characterization of nested mean regressions. We then apply a recursive Riesz representer estimation learning algorithm that estimates de-biasing corrections without the need to characterize how the correction terms look like, such as for instance, products of inverse probability weighting terms, as is done in prior work on doubly robust estimation in the dynamic regime. Our approach defines a sequence of loss minimization problems, whose minimizers are the mulitpliers of the de-biasing correction, hence circumventing the need for solving auxiliary propensity models and directly optimizing for the mean squared error of the target de-biasing correction. We provide further applications of our approach to estimation of dynamic discrete choice models and estimation of long-term effects with surrogates.
