`Collaborative Research: Estimation for and Inference on Causal Effects

这个项目的总体目标是增进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的项目。第一部分包括项目评估中的三个主题。在关于平均处理效应估计的文献中，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。通常情况下，研究人员没有给出具体的数据驱动的选择，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对照单元的协变量分布之间的重叠相关的问题。这种重叠的缺乏导致了文献中使用的大多数估计量的高度敏感性，并导致研究人员使用特别的方法来施加足够的重叠。将制定一个原则性和最优化的样本选择标准。第三，将考虑匹配估计量的自举方法。简单的Bootstrap将被证明不适用于匹配估计器，并将研究可能具有更好特性的替代方案。在第二个项目中，计划研究同伴效应的聚合影响。例如，如果学校同学的特点或行为影响了个人的结果，那么重新分配个人到班级可能会产生个人和集体的影响。虽然许多文献都集中在个体水平效应的识别和估计上，但在当前的提议下，将开发新的方法来估计和推断总体影响。在第三个项目中，研究人员计划调查在存在同伴效应或实验单位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，标准的实验方法就不适用。研究人员建议研究这种设置下的Fisher精确检验的等价物，以及构建可信区间的Neyman方法的等价物。初步结果表明，一般来说，这些方法在有相互作用的情况下并不像在没有相互作用的情况下那样相似。拟议活动产生的广泛影响是可以获得现成的软件，用于评估包括经济学和其他社会科学在内的学术领域以及政策制定者的因果影响。将用MatLab和Stata开发软件，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.