`Collaborative Research: Estimation for and Inference on Causal Effects

`合作研究：因果效应的估计和推断

• 批准号: 0452590
• 负责人: Guido Imbens
• 金额: $ 19.57万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-03-15 至 2006-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0452590&HistoricalAwards=false
• 关键词: Collaborative; Research; Estimation; Inference; Causal

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.

这个项目的总体目标是提高对因果效应的估计和推断的认识。 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。在估计平均治疗效果的文献中，已经提出了几种有效的估计量，这些估计量需要选择平滑参数，例如，内核回归中的带宽。通常，研究人员没有给出具体的数据驱动的选择，限制了这些估计的适用性。这里，将导出用于平滑参数的最佳数据驱动选择。其次，将分析与治疗单位和对照单位的协变量分布重叠相关的问题。缺乏这种重叠导致大量的敏感性，在文献中使用的大多数估计，并导致研究人员使用特设的方法施加足够的重叠。将制定一个原则性和最佳的标准来选择样本。第三，将考虑匹配估计的自举方法。 简单的自举将被证明是无效的匹配估计，并可能有更好的性能的替代品将被studied.In第二个项目的计划是研究同行效应的总影响。如果特点或行动，例如，同学在学校影响一个人的结果，可能有个人和集体的影响，重新分配个人的类。虽然大部分的文献都集中在识别和估计个人水平的影响，根据目前的建议，将开发新的方法来估计和推断的总体效果。研究者们将研究完全正向连续匹配（将某些特征的最高值的个体分组在一起）的效果的推断，以及向更正向连续匹配移动的效果的测试。在第三个项目中，研究者们计划研究在存在同伴效应或实验单元之间的相互作用的情况下，随机实验的设计和分析。如果单元相互作用，则标准实验方法不适用。研究人员建议研究这种设置的Fisher精确检验的等效方法，以及Neyman构建置信区间的等效方法。初步结果表明，在一般情况下，这些方法是不相似的情况下，相互作用的情况下，在没有。更广泛的影响，从拟议的活动所产生的是现成的使用软件的可用性，在学术领域，包括经济学和其他社会科学的因果关系的影响进行评估，并为决策者。将用Matlab和STATA开发软件，供其他研究人员使用。