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Data integration in causal inference
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DOI：10.1002/wics.1581.png)
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摘要
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Integrating data from multiple heterogeneous sources has become increasingly popular to achieve a large sample size and diverse study population. This article reviews development in causal inference methods that combines multiple datasets collected by potentially different designs from potentially heterogeneous populations. We summarize recent advances on combining randomized clinical trials with external information from observational studies or historical controls, combining samples when no single sample has all relevant variables with application to two-sample Mendelian randomization, distributed data setting under privacy concerns for comparative effectiveness and safety research using real-world data, Bayesian causal inference, and causal discovery methods. This article is categorized under: Statistical Models > Semiparametric Models Applications of Computational Statistics > Clinical Trials
Keyword:
causal inference
data fusion
data integration
generalizability
transportability
期刊
W
IF:
5.4
论文数:
201
被引数:
5.1K
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