Annual Review of Statistics and Its ApplicationNonprobability Sampling and Causal Analysis

Ulrich Kohler; Frauke Kreuter; Elizabeth A. Stuart

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Annual Review of Statistics and Its ApplicationNonprobability Sampling and Causal Analysis

机译：统计年度审查及其应用程序的应用抽样和因果分析

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The long-standing approach of using probability samples in social science research has come under pressure through eroding survey response rates, advanced methodology, and easier access to large amounts of data. These factors, along with an increasedawareness of the pitfalls of the nonequivalent comparison group design for the estimation ofcausal effects, have moved the attention of applied researchers away from issues of sampling and toward issues of identification. This article discusses the usability of samples with unknown selection probabilities for various research questions. In doing so, we review assumptions necessary for descriptive and causal inference and discuss research strategies developed to overcome sampling limitations.

机译：通过侵蚀调查响应率，先进方法，更容易获得大量数据，在社会科学研究中使用概率样本的长期途径已经受到压力。这些因素以及非等价性比较小组设计的陷阱损害的损害率增加了估算，从而引起了所应用的研究人员远离取样问题和识别问题。本文讨论了各种研究问题的未知选择概率的样本的可用性。这样做，我们审查了描述性和因果推论所需的假设，并讨论为克服采样限制而开发的研究策略。

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《Annual Review of Statistics and Its Application》 |2019年第2019期|共24页
作者
Ulrich Kohler; Frauke Kreuter; Elizabeth A. Stuart;
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作者单位

Faculty of Economics and Social Sciences University of Potsdam 14482 Potsdam Germany;

Joint Program in SurveyMethodology UniversityofMaryland College Park Maryland 20742 USA;

Department of Mental Health Department of Biostatistics and Department of Health Policy and Management Bloomberg School ofPublic Health Johns Hopkins University Baltimore Maryland 21205 USA;

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原文格式 PDF
正文语种 eng
中图分类统计学;
关键词
causal inference; generalizability; self-selection; nonprobability sampling; validity; measurement error; heterogeneous treatment effects; big data;

机译：因果推断;概括;自我选择;非可行性采样;有效性;测量误差;异构治疗效果;大数据;

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Annual Review of Statistics and Its ApplicationNonprobability Sampling and Causal Analysis

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