Inference for Average Treatment Effects

对政策干预（如职业培训计划）的因果效应的估计是许多应用经济研究的一个重要目标。通常一个合理的出发点是假设治疗的分配是随机的，基于足够详细的预处理变量。在这个假设下，人们可以确定人口平均效应.本研究将分三个部分对这些假设下的平均处理效果的推断文献做出贡献。首先，本文的研究将发展匹配估计量的大样本理论。匹配估计量是指每个处理单元与一个或固定数量的对照匹配，每个对照与一个或固定数量的处理单元匹配的估计量。这种纯匹配估计具有相当大的直观吸引力，并已在实践中得到广泛的应用，除了特殊情况外，它们的大样本理论尚未建立。结果应该是这种匹配估计的渐近理论，使研究人员能够在实践中使用这些估计。在第二部分，研究将调查高阶properties的一些估计的平均治疗效果，已提出。许多这些估计有一个非参数的组成部分。然而，大多数的文献是沉默的关于平滑参数的实际选择，超越率条件。这使得从业者很难真正实现这些估计。这里的计划是todeveloping均方误差为基础的标准，以获得一个明确的数据驱动的标准为平滑参数。在第三部分中，本研究将比较一些估计平均治疗效果。到目前为止，已经提出了一些估计，往往是一个小的模拟研究，以调查他们的属性。这项研究所完成的伊萨对各种估计方法的系统比较。在许多社会项目的研究中，如职业培训项目，观察数据被用来评估这些项目。这种评估的统计方法通常依赖于匹配类型的方法，将受训者与类似的对照组相匹配，即接受培训的个人与没有接受培训的个人具有相似的背景特征和劳动力市场历史。目前使用了各种各样的方法，但这些方法的性能和可靠性往往未知。本文研究了这类方法的形式性质。此外，这项研究将开发自动化的程序来实现其中的一些方法.目前，这些方法往往需要研究人员在实施中做出一些可能会影响最终结果的选择，而没有太多的指导来指导这些选择。这将使这些方法更加透明，更容易实现。最后，该研究将在已知正确答案的情况下比较这些方法中的一些，以评估其性能并提出建议，为未来使用这些方法提供信息。

Estimation of causal effects of policy interventions such as job training programs is animportant goal of much applied economic research. Often a reasonable starting point is theassume that assignment to the treatment is random given on sufficiently detailed observedpretreatment variables. Under that assumption one can identify the population averageeffect. This research will contribute to the literature on inference for average treatments effects underthese assumptions in three parts. First, the research will developlarge sample theory for matching estimators. By matching estimators we mean estimatorswhere each treated unit is matched to one or a fixed number of controls, and each controlis matched to one or a fixed number of treated units. Such pure matching estimators haveconsiderable intuitive appeal and have been used widely in practice, without their largesample theory having been established other than for special cases. The result should bean asymptotic theory for such matching estimators that allows researchers to use theseestimators in practice. In a second part, the research will investigate higher order propertiesof some of the estimators for average treatment effects that have been proposed. Manyof these estimators have a nonparametric component. However, most of the literature issilent regarding the actual choice of smoothing parameters, beyond rate conditions. Thismakes it di .cult for practitioners to actually implement thse estimators. Here the plan is todevelop a mean-squared-error based criterion to derive an explicit data-driven criterion forthe smoothing parameter. In the third part, the research will compare a number of theestimators for average treatment effects. So far a number of estimators have been proposed,often with a small simulation study to investigate their properties. What this research accomplishes isa systematic comparison of various estimators.In many studies of social programs such as job training programs observational dataare used to evaluate these programs. Statistical methods for such evaluations often rely onmatching type methods that match trainees to similar controls, that is individuals whoreceived the training to individuals who did not receive the training with similar backgroundcharacteristics and labor market histories. A variety of such methods are currently used, withoften the properties and reliability of such methods unknown. This research investigates theformal properties of such methods. In addition the research will develop automated procedures forimplementing some of these methods. Currently these methods often require the researcherto make a number of choices in the implementation that potentially affect the final resultssubstantially, without much guidance available to guide these choices. This should makethese methods more transparent and easier to implement. Finally, the research will compare a numberof these methods in settings where the correct answers are known so as to evaluate theirperformance and reach recommendations to inform the future use of such methods.