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

职业：复杂实验中因果推理的基于设计的视角

• 批准号: 1945136
• 负责人: Peng Ding
• 金额: $ 40万
• 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: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1945136&HistoricalAwards=false
• 关键词: CAREER; Design; Based; Perspective; Causal

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.

随机化实验已广泛应用于农业、工业和临床试验中。R. A. Fisher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。来自社会科学和技术公司的现代应用程序具有更丰富的协变量和更复杂的时间和层次结构。受这些新应用的启发，PI将推进现代实验的设计和分析的理论和方法，以在各种环境中进行稳健的治疗效果估计。突出实验设计的作用，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实验组之间的协变量，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具有鲁棒性。这些实验的理论结果也将阐明观察性研究的原则分析，其中控制实验是不可行的。培训部分包括研究生和本科生的课程工作，以及通过本科生和研究生的帮助下开发软件。这构成了一个强有力的计划，以整合研究和教育。因果推理的设计为基础的角度不假设任何强有力的结果建模假设，并侧重于治疗分配机制，可以由实验者决定。从这个角度来看，PI将改进现有的实验设计，以获得更好的协变量平衡，并在违反相应的模型假设时评估许多基于模型的程序。PI将首先提出并分析区组、序贯和析因设置中的重新随机化，重点关注治疗效应估计量的重复采样特性，并讨论有和无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，包括有和无不依从性的病例。此外，PI将使用目标弱零假设校准随机化检验，并根据对具有协变量的完全随机化实验和精细分层实验的详细分析，提出具有稳健有效协变量调整的随机化检验。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将开发和传播实现这些方法的开源R软件包。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

To Adjust or not to Adjust? Estimating the Average Treatment Effect in Randomized Experiments with Missing Covariates

• DOI: 10.1080/01621459.2022.2123814
• 发表时间: 2021-07
• 期刊: Journal of the American Statistical Association
• 影响因子: 3.7
• 作者: Anqi Zhao;Peng Ding

Multiply robust estimation of causal effects under principal ignorability

主可忽略性下因果效应的乘法稳健估计

• DOI: 10.1111/rssb.12538
• 发表时间: 2022
• 期刊: Journal of the Royal Statistical Society: Series B (Statistical Methodology
• 作者: Jiang, Zhichao;Yang, Shu;Ding, Peng
• 通讯作者: Ding, Peng

Regression-based causal inference with factorial experiments: estimands, model specifications and design-based properties

基于回归的因果推理与阶乘实验：估计值、模型规范和基于设计的属性

• DOI: 10.1093/biomet/asab051
• 发表时间: 2021
• 期刊: Biometrika
• 影响因子: 2.7
• 作者: Zhao, Anqi;Ding, Peng
• 通讯作者: Ding, Peng

Regression adjustment in completely randomized experiments with a diverging number of covariates

协变量数量不同的完全随机实验中的回归调整

• 发表时间: 2021
• 期刊: Biometrika
• 影响因子: 2.7
• 作者: Lei, L. and

Randomization Tests for Weak Null Hypotheses in Randomized Experiments

随机实验中弱零假设的随机检验

• DOI: 10.1080/01621459.2020.1750415
• 发表时间: 2020
• 期刊: Journal of the American Statistical Association
• 影响因子: 3.7
• 作者: Ding, P