Inference for Average Treatment Effects

估计就业培训计划等政策干预的因果效应是许多应用经济学研究的一个重要目标。通常一个合理的起点是假设对治疗的分配是随机给予足够详细的观察到的前处理变量。在这一假设下，人们可以确定总体平均效应。这项研究将在三个部分对这些假设下的平均处理效应的推断的文献做出贡献。首先，研究将发展匹配估计量的样本理论。所谓匹配估计量，我们指的是估计量，其中每个处理单位与一个或固定数量的对照相匹配，每个对照与一个或固定数量的处理单位相匹配。这种纯匹配估计具有相当大的直观吸引力，并在实践中得到了广泛的应用，但除了特殊情况外，还没有建立起它们的大量样本理论。结果应该是这样的匹配估计量的渐近理论，以便研究人员在实践中使用这些估计量。在第二部分中，研究将调查一些已提出的平均处理效应估计量的高阶性质。这些估计量中的许多都有非参数成分。然而，除了速率条件外，大多数文献都没有关于平滑参数的实际选择。这使得从业者很难真正实现这些估计器。这里的计划是开发一个基于均方误差的准则，以推导出一个显式的数据驱动的平滑参数准则。在第三部分中，研究将比较一些平均治疗效果的估计值。到目前为止，已经提出了许多估计器，通常通过小型模拟研究来研究它们的性质。本研究完成的是对不同估计者的系统比较。在许多关于社会项目的研究中，如职业培训项目，都是使用观测数据来评估这些项目。这种评估的统计方法通常依赖于将受训者与相似的对照相匹配的类型方法，即接受培训的个人与没有接受培训的具有相似背景特征和劳动力市场历史的个人。目前使用的方法有很多，但这些方法的性质和可靠性往往不得而知。本研究探讨了这类方法的形式性质。此外，这项研究还将开发实施其中一些方法的自动化程序。目前，这些方法往往要求研究人员在实施过程中做出一些选择，这些选择可能会对最终结果产生重大影响，而没有太多指导来指导这些选择。这应该会使这些方法更透明、更容易实现。最后，这项研究将在已知正确答案的情况下对几种方法进行比较，以评估它们的性能，并得出建议，为未来使用这些方法提供参考。

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.