论文标题
基于功率分析，改善干扰下的随机测试
Improving Randomization Tests under Interference Based on Power Analysis
论文作者
论文摘要
在因果推论中，我们可以考虑一种情况，即一种单位上的治疗会影响他人，即存在干扰。在干扰存在的情况下，我们无法直接执行经典的随机测试，因为零假设并不清晰。取而代之的是，我们需要执行仅限于单位和分配的子集的随机测试，从而使无效假设敏锐。一项先前的研究通过将适当的子集的选择选择在两部分图中搜索双石，构建了一种有用的测试方法，即双比克测试。但是，由于功率取决于所选子集的功能，因此仍然存在通过完善选择过程来提高功率的空间。在本文中，我们提出了一种基于随机测试的功率评估来改善双石测试的方法。我们在几个假设下明确得出了随机测试的功率的表达式，发现从给定的分配集计算出的一定数量表征了功率。基于这一事实，我们提出了一种通过修改单位和任务子集的选择规则来提高双石测试功能的方法。通过具有空间干扰设置的模拟，我们确认所提出的方法具有比现有方法更高的功率。
In causal inference, we can consider a situation in which treatment on one unit affects others, i.e., interference exists. In the presence of interference, we cannot perform a classical randomization test directly because a null hypothesis is not sharp. Instead, we need to perform the randomization test restricted to a subset of units and assignments that makes the null hypothesis sharp. A previous study constructed a useful testing method, a biclique test, by reducing the selection of the appropriate subsets to searching for bicliques in a bipartite graph. However, since the power depends on the features of selected subsets, there is still room to improve the power by refining the selection procedure. In this paper, we propose a method to improve the biclique test based on a power evaluation of the randomization test. We explicitly derived an expression for the power of the randomization test under several assumptions and found that a certain quantity calculated from a given assignment set characterizes the power. Based on this fact, we propose a method to improve the power of the biclique test by modifying the selection rule for subsets of units and assignments. Through a simulation with a spatial interference setting, we confirm that the proposed method has higher power than the existing method.
