论文标题
结果假设和双重性理论平衡重量
Outcome Assumptions and Duality Theory for Balancing Weights
论文作者
论文摘要
我们研究了平衡体重估计器，从源人群重新获得了估计目标人群的失踪结果。这些估计器通过对结果模型做出假设来最大程度地减少最坏情况。在本文中，我们表明这种结果假设具有两个直接的含义。首先，我们可以替换平衡权重与假定结果函数类别的简单凸损失的最小值优化问题。其次，我们可以用更合适的定量度量（最小最坏情况偏差）代替常见的重叠假设。最后，我们显示了当我们对结果的假设是错误的情况下，权重仍保持强大的条件。
We study balancing weight estimators, which reweight outcomes from a source population to estimate missing outcomes in a target population. These estimators minimize the worst-case error by making an assumption about the outcome model. In this paper, we show that this outcome assumption has two immediate implications. First, we can replace the minimax optimization problem for balancing weights with a simple convex loss over the assumed outcome function class. Second, we can replace the commonly-made overlap assumption with a more appropriate quantitative measure, the minimum worst-case bias. Finally, we show conditions under which the weights remain robust when our assumptions on the outcomes are wrong.
