Improving representativeness in non-probability surveys and causal inference with regularized regression and post-stratification

通过正则化回归和后分层提高非概率调查和因果推断的代表性

• 批准号: 10219956
• 负责人: ANDREW GELMAN
• 金额: $ 25.4万
• 依托单位: COLUMBIA UNIV NEW YORK MORNINGSIDE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10219956
• 关键词: Accounting; Area; Critiques; Data; Data Set; Diagnostic; Ensure; Equilibrium; Health; Heterogeneity; Individual; Intervention; Investigation; Judgment; Methodology; Methods; Modeling; Nature; Policy Maker; Population; Population Statistics; Probability; Probability Samples; Process; Public Health; Public Policy; Research; Research Personnel; Rest; Sampling; Singapore; Social Sciences; Statistical Methods; Stratification; Structure; Subgroup; Surveys; Techniques; Telephone; Testing; Trust; Uncertainty; Validation; Weight; Work; base; health data; high dimensionality; improved; innovation; meetings; population based; response; statistics; success; tool; treatment effect; treatment group; trend; trustworthiness

Project Summary/Abstract The proposed project has a broad aim of working with the increasing complexities of survey statistics with de- creasing response rate. We focus specifically on non-probability samples (samples of convenience) due to their increasing popularity, but note that these non-probability samples are simply an extreme case of a probability based survey with high non-response, and so our methods could be expected to generalize. Long term, our hope is to find methods and techniques to safely adjust non-probability samples to a wider population whilst concurrently developing methods of critiquing these estimates to increase researcher, policy maker and public confidence in these estimates. Our specific aims focus in on developing the tools and techniques to make this possible. We focus primarily on a regularized regression and poststratification methodology that has already shown some success with non- representative and even convenience samples. Using this methodology, we focus on adaptions that make this technique useful for public health settings. Specifically we focus on a three pronged approach. Firstly, we aim to make adaptions to the current state of the arc of modelling technique to better suit the unique challenges posed by public health datasets and questions. Our approach to achieve this is to focus on partial pooling with more structured adjustment variables, and more broadly considering high dimensional variables with continuous and non-continuous components. Not only that, but we move to also consider uncertainty in poststratification, namely when adjusting for variables not known in the population. In a complementary approach, we also aim to assess coverage by combining raw survey data but assuming differences in sample. Secondly, we note that many our central methodology could be extended to questions of a causal nature. This is particularly relevant to public health challenges because often causal estimates are desired. Our approach is to extend the model based approach to assume heterogeneity of effect within demographic subgroups. Then by using regularization, the effect within each subgroup is estimated and used to poststratify to the population. Groups with relatively few treated/untreated individuals would be estimated with greater uncertainty, which is an innovative approach to accounting for balance. Thirdly and finally we note that the regularized regression and prediction technique is particularly reliant on model assumptions. Our final aim is to consider methods of testing and validating models with non-representative data in order to obtain better and more trustworthy population based estimates.

项目总结/摘要 拟议的项目有一个广泛的目标，即处理日益复杂的调查统计数据， 提高响应率。我们特别关注非概率样本（方便样本），因为它们 越来越受欢迎，但请注意，这些非概率样本只是概率的极端情况 基于高无应答率的调查，因此我们的方法可以预期推广。长期来看，我们 希望找到方法和技术，安全地调整非概率样本，以更广泛的人口，同时 同时开发批评这些估计的方法，以增加研究人员，政策制定者和公众 这些估计的可信度。 我们的具体目标集中在开发工具和技术，使之成为可能。我们主要关注 基于正则化回归和后分层方法，该方法已经在非线性领域取得了一些成功， 代表性的，甚至是方便的样品。使用这种方法，我们专注于适应， 对公共卫生环境有用的技术。 具体而言，我们专注于三管齐下的方法。首先，我们的目标是适应当前的状态， 建模技术的弧线，以更好地适应公共卫生数据集和问题所带来的独特挑战。 我们实现这一目标的方法是关注具有更多结构化调整变量的部分池化， 广义地考虑具有连续和非连续分量的高维变量。不仅如此， 但我们也要考虑后分层的不确定性，即在调整未知变量时， 人口。作为补充，我们还旨在通过结合原始调查数据来评估覆盖率， 假设样本存在差异。 第二，我们注意到，我们的许多核心方法可以扩展到因果性质的问题。这 与公共卫生挑战特别相关，因为通常需要因果估计。我们的做法是 扩展基于模型的方法，以假设人口统计学亚组内效应的异质性。然后 通过使用正则化，估计每个子组内的效果，并用于对总体进行后分层。 治疗/未治疗个体相对较少的组将以更大的不确定性进行估计，这是一个 以创新的方法核算余额。 第三和最后，我们注意到正则化回归和预测技术特别依赖于 模型假设。我们的最终目标是考虑测试和验证非代表性模型的方法 数据，以获得更好和更可靠的人口为基础的估计。