CAREER: The Design-Based Perspective of Causal Inference in Complex Experiments

随机试验已广泛应用于农业、工业和临床试验。R.A.费舍尔正式讨论了随机化在实验中的价值：它平均平衡了观察到的和未观察到的协变量，并作为统计推断的基础。然而，经典的结果仅限于简单的实验，没有丰富的协变量和复杂的时间和层次结构。源于社会科学和技术公司的现代应用具有更丰富的协变量和更复杂的时间和层次结构。在这些新应用的推动下，PI将推动现代实验的设计和分析的理论和方法，以在不同的环境中进行稳健的治疗效果评估。强调实验设计的作用，PI将采取连贯的基于设计的因果推理的观点。特别是，PI将提出各种新的实验设计，这些设计可以更好地平衡试验组之间的协变量，并为这些设计开发统计方法，这些设计对结果生成过程的模型假设是稳健的。这些实验的理论结果也将有助于对控制实验不可行的观察性研究的原则性分析。培训部分包括研究生和本科课程工作，以及通过本科生和研究生的帮助开发软件。这构成了一个将研究和教育相结合的强有力的计划。基于设计的因果推理视角没有假设任何强有力的结果建模假设，而是专注于可以由实验者确定的治疗分配机制。在这一视角下，PI将改进现有的实验设计，使其具有更好的协变量平衡，并在相应的模型假设可能被违反时评估许多基于模型的程序。PI将首先提出并分析分块、序贯和因子设置下的再随机化，重点讨论治疗效果估计量的重复抽样性质，并讨论有和没有协变量调整的估计量。然后，PI将提出并分析治疗效果的线性和非线性协变量调整估计值，包括有和没有不依从性的情况。此外，PI将基于对带有协变量的完全随机化实验和精细分层实验的详细分析，校准具有针对性的弱零假设的随机化测试，并提出具有稳健和有效的协变量调整的随机化测试。PI还将建立基于随机化的推理框架和程序，用于时间和层次结构的实验。最后，PI将开发和传播实现方法的开源R软件包。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Randomized experiments have been widely used in agriculture, industry, and clinical trials. R. A. Fisher formally discussed the value of randomization in experiments: it balances observed and unobserved covariates on average and serves as a basis for statistical inference. Classical results, however, are limited to simple experiments without rich covariates and complex time and hierarchical structures. Modern applications stemming from the social sciences and technology companies have richer covariates and more complex time and hierarchical structures. Motivated by these new applications, the PI will advance the theories and methodologies for the design and analysis of modern experiments for robust treatment effect estimation in various settings. Highlighting the role of the design of experiments, the PI will take a coherent design-based perspective of causal inference. In particular, the PI will propose various new experimental designs that can better balance covariates across experimental groups and develop statistical methods for these designs that are robust to model assumptions on the outcome generating processes. These theoretical results for experiments will also shed light on principled analyses of observational studies where controlled experiments are infeasible.  The training component includes graduate and undergraduate course work as well as the development of software through the help of both undergraduate and graduate students. This constitutes a strong plan to integrate research and education. The design-based perspective of causal inference does not assume any strong outcome modeling assumptions and focuses on the treatment assignment mechanism that can be determined by the experimenters. Under this perspective, the PI will improve existing experimental designs to have better covariate balance and evaluate many model-based procedures when the corresponding model assumptions can be violated. The PI will first propose and analyze rerandomization in blocking, sequential and factorial settings, focusing on repeated sampling properties of the treatment effect estimators and discussing the estimators with and without covariate adjustment. The PI will then propose and analyze linear and nonlinear covariate-adjusted estimators for treatment effects, including the cases with and without noncompliance. Moreover, the PI will calibrate randomization tests with targeted weak null hypotheses and propose randomization tests with robust and efficient covariate adjustment, based on detailed analyses of completely randomized experiments with covariates and finely stratified experiments. The PI will also establish randomization-based inferential frameworks and procedures for experiments with time and hierarchical structures. Finally, the PI will develop and disseminate open-source R software packages that implement the methodologies.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.