# MathSciDoc: An Archive for Mathematician ∫

#### Gold Award Paper in 2017

Journal of the American Statistical Association, 2017
Frequentists' inference often delivers point estimators associated with con dence intervals or sets for parameters of interest. Constructing the con dence intervals or sets requires understanding the sampling distributions of the point estimators, which, in many but not all cases, are related to asymptotic Normal distributions ensured by central limit theorems. Although previous literature has established various forms of central limit theorems for statistical inference in super population models, we still need general and convenient forms of central limit theorems for some randomization-based causal analysis of experimental data, where the parameters of interests are functions of a nite population and randomness comes solely from the treatment assignment. We use central limit theorems for sample surveys and rank statistics to establish general forms of the nite population central limit theorems that are particularly useful for proving asymptotic distributions of randomization tests under the sharp null hypothesis of zero individual causal e ects, and for obtaining the asymptotic repeated sampling distributions of the causal e ect estimators. The new central limit theorems hold for general experimental designs with multiple treatment levels, multiple treatment factors and vector outcomes, and are immediately applicable for studying the asymptotic properties of many methods in causal inference, including instrumental variable, regression adjustment, rerandomization, clustered randomized experiments, and so on. Previously, the asymptotic properties of these problems are often based on heuristic arguments, which in fact rely on general forms of nite population central limit theorems that have not been established before. Our new theorems ll in this gap by providing more solid theoretical foundation for asymptotic randomization-based causal inference.
```@inproceedings{xinran2017general,
title={General forms of finite population central limit theorems with applications to causal inference},
author={Xinran Li, and Peng Ding},
url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20170622133455757300791},
booktitle={Journal of the American Statistical Association},
year={2017},
}
```
Xinran Li, and Peng Ding. General forms of finite population central limit theorems with applications to causal inference. 2017. In Journal of the American Statistical Association. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20170622133455757300791.