政策选择的实验设计
Experimental Design for Policy Choice
浏览论文内容
中文总结 AI 辅助
本文提出在政策选择前优化第二波实验设计的方法，基于极限实验近似并证明渐近最优，应用于有条件现金转移实验以展示潜在巨大收益。
中文摘要 AI 辅助
我们展示了当所得数据将用于在约束条件下选择福利最大化的政策时，如何最优地设计实验。决策者旨在通过选择其效果依赖于未知有限维参数的政策来最大化贝叶斯期望福利。决策者可获得第一波具有固定设计的实验数据，但可以选择在选定政策之前收集的第二波实验的设计。由此产生的实验设计-政策选择问题是一个极高维的动态规划问题，在有限样本中通常是难以处理的。我们提出了一种基于极限实验的可处理近似方法，并利用一个新的针对连续处理的自适应实验的渐近表示定理证明了其渐近最优性。我们将该方法应用于一项有条件现金转移实验，并展示了根据政策选择定制实验可能带来的巨大收益。
英文摘要
We show how to optimally design experiments when the resulting data will be used to choose a welfare-maximizing policy subject to constraints. A decision maker seeks to maximize Bayes expected welfare by choosing a policy whose effects depend on an unknown finite-dimensional parameter. The decision maker has access to a first wave of experimental data with a fixed design but may choose the design of a second wave that will be collected before choosing the policy. The resulting experimental design--policy choice problem is a very high-dimensional dynamic program that is generally intractable in finite samples. We propose a tractable approximation based on the limit experiment and show it is asymptotically optimal using a new asymptotic representation theorem for adaptive experiments with continuous treatments. We apply the method to a conditional cash transfer experiment and demonstrate the potential for large gains from tailoring the experiment to the policy choice.
