`Collaborative Research: Estimation for and Inference on Causal Effects

该项目的总体目标是增进对因果效应的估计和推断的了解。 研究人员提出了三个具体项目。第一个包括项目评估的三个主题。在估计平均治疗效果的文献中，提出了几种有效的估计器，这些估计器需要选择平滑参数，例如核回归中的带宽。通常，研究人员没有给出具体的数据驱动选择，限制了这些估计量的适用性。这里将导出平滑参数的最佳数据驱动选择。其次，将分析与处理单元和控制单元的协变量分布之间重叠相关的问题。缺乏这种重叠导致文献中使用的大多数估计量都非常敏感，并导致研究人员使用临时方法来施加足够的重叠。将制定选择样本的原则性最佳标准。第三，将考虑匹配估计器的引导方法。 简单的引导程序将被证明对于匹配估计量无效，并且将研究可能具有更好属性的替代方案。在第二个项目中，计划是研究同伴效应的总体影响。例如，如果学校同学的特征或行为影响个人的结果，则将个人重新分配到班级可能会产生个人和集体影响。尽管许多文献都集中在个体水平影响的识别和估计上，但根据当前的提议，将开发新的方法来估计和推断总体影响。将研究完全正选配匹配效果的推论（其中某些特征值最高的个体被分组在一起），以及对转向更正选配匹配的效果的测试。在第三个项目中，研究人员计划研究在存在同伴效应或实验单元之间相互作用的情况下随机实验的设计和分析。如果单位相互作用，则标准实验方法不适用。研究人员建议研究这种设置下的费舍尔精确检验的等效方法，以及构建置信区间的内曼方法的等效方法。初步结果表明，总的来说，这些方法在有相互作用的情况下并不像没有相互作用的情况下那样相似。拟议活动产生的更广泛的影响是，可以使用现成的软件来评估学术领域（包括经济学和其他社会科学）和政策制定者的因果效应。软件将在 Matlab 和 STATA 中开发，可供其他研究人员使用。

The overall goal of this project is to advance knowledge on the estimation of and inference for causal effects. The researchers propose three specific projects. The first consists of three topics in program evaluation. In the literature on estimation of average treatment effects several efficient estimators have been proposed that require choices for smoothing parameters, e.g., the bandwidth in kernel regression. Typically researchers have not given specific data-driven choices, limiting the applicability of these estimators. Here an optimal data-driven choice for the smoothing parameter will be derived. Second, issues related to overlap between covariate distributions for treated and control units will be analyzed. The lack of such overlap leads to substantial sensitivity of most of the estimators used in the literature and has led researchers to use ad hoc methods for imposing sufficient overlap. A principled and optimal criterion for selecting the sample will be developed. Third, bootstrapping methods for matching estimators will be considered. The simple bootstrap will be shown not to be valid for matching estimators, and alternatives that may have better properties will be studied.In the second project the plan is to study the aggregate implications of peer effects. If characteristics or actions of, for example, classmates in school affect an individual's outcome, there may be both individual and aggregate effects of reassigning individuals to classes. Whereas much of the literature has focused on the identification and estimation of individual level effects, under the current proposal new methods will be developed for estimation and inference for the aggregate effects. Inference for the effect of full positive assortive matching (where the individuals with the highest values of some characteristic are grouped together), as well as tests for the effect of moving towards more positive assortive matching will be studied.In the third project the researchers plan to investigate the design and analysis of randomized experiments in the presence of peer effects or interactions between the experimental units. If units interact, standard experimental methods do not apply. The researchers propose to study the equivalent of Fisher exact tests for this setting, as well as the equivalent of Neyman's methods for constructing confidence intervals. Preliminary results show that in general these methods are not as similar to each other in the case with interaction as in the case without.Broader impacts resulting from the proposed activity are the availability of ready-to-use software for the evaluation of causal effects in both academic areas, including economics and other social sciences, and for policy makers. Software will be developed in Matlab and STATA that will be made available to other researchers.