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Nonprobability Sampling and Causal Analysis
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DOI：10.1146/annurev-statistics-030718-104951.png)
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摘要
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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 increased awareness of the pitfalls of the nonequivalent comparison group design for the estimation of causal 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.
Keyword:
causal inference
generalizability
self-selection
nonprobability sampling
validity
measurement error
heterogeneous treatment effects
big data
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论文数:
211
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