Inference from Nonrandom Samples Using Bayesian Machine Learning.
Inference from Nonrandom Samples Using Bayesian Machine Learning.
复制标题
使用贝叶斯机器学习从非随机样本进行推断。
DOI：
10.1093/jssam/smab049
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发表时间：
2023
影响因子：
2.1
通讯作者：
Chen,Qixuan
中科院分区：
文献类型：
--
作者：
Liu,Yutao;Gelman,Andrew;Chen,Qixuan
我们认为，在数据丰富的设置，高维辅助信息是在样本和目标人群中，调查推断是一个特殊的情况下，从非随机样本的推断。我们提出了一种正则化的预测方法，使用大量的辅助变量预测人口中的结果，这样的可验证性假设是合理的，贝叶斯框架是直接量化的不确定性。除了辅助变量之外，我们还通过估计样本中包含的单元的倾向得分来扩展该方法，并将其作为机器学习模型中的预测因子。我们发现，在模拟研究中，使用软贝叶斯加性回归树的正则化预测产生有效的推断人口的手段和覆盖率接近名义水平。我们证明了所提出的方法使用两种不同的真实的数据应用程序，一个在调查和流行病学研究之一的应用。
We consider inference from nonrandom samples in data-rich settings where high-dimensional auxiliary information is available both in the sample and the target population, with survey inference being a special case. We propose a regularized prediction approach that predicts the outcomes in the population using a large number of auxiliary variables such that the ignorability assumption is reasonable and the Bayesian framework is straightforward for quantification of uncertainty. Besides the auxiliary variables, we also extend the approach by estimating the propensity score for a unit to be included in the sample and also including it as a predictor in the machine learning models. We find in simulation studies that the regularized predictions using soft Bayesian additive regression trees yield valid inference for the population means and coverage rates close to the nominal levels. We demonstrate the application of the proposed methods using two different real data applications, one in a survey and one in an epidemiologic study.