`Collaborative Research: Estimation for and Inference on Causal Effects

这个项目的总体目标是提高对因果效应的估计和推断的知识。研究人员提出了三个具体项目。第一部分包括项目评估的三个主题。在关于平均处理效果估计的文献中，已经提出了几个需要选择平滑参数的有效估计器，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，限制了这些估计器的适用性。这里将推导出平滑参数的最优数据驱动选择。其次，将分析与处理单元和控制单元的协变量分布重叠有关的问题。这种重叠的缺乏导致了文献中使用的大多数估计器的实质性敏感性，并导致研究人员使用临时方法来施加足够的重叠。一个原则性和最佳的标准选择的样本将开发。第三，将考虑匹配估计量的自举方法。简单的自举将被证明对匹配估计器无效，并且将研究可能具有更好性质的替代方法。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到个人的结果，那么将个人重新分配到班级可能会产生个人和集体效应。尽管许多文献都集中在个体水平效应的识别和估计上，但根据目前的建议，将开发新的方法来估计和推断总体效应。将研究完全正分类匹配效应的推断（某些特征值最高的个体被分组在一起），以及向更积极分类匹配移动的效果的测试。在第三个项目中，研究人员计划在存在同伴效应或实验单位之间相互作用的情况下调查随机实验的设计和分析。如果单元相互作用，标准实验方法不适用。研究人员建议在这种情况下研究等价于Fisher精确检验的方法，以及等价于Neyman构建置信区间的方法。初步结果表明，在一般情况下，这些方法在有相互作用的情况下的相似性不如没有相互作用的情况。拟议的活动所产生的更广泛的影响是，在包括经济学和其他社会科学在内的学术领域，以及为决策者提供现成的软件，以评估因果关系。软件将在Matlab和STATA中开发，并将提供给其他研究人员。

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.