Data integration in causal inference
Copyright policy )Data integration in causal inference
AbstractIntegrating 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
- UNIVERSITY OF MICHIGAN AT ANN ARBOR
- UNIVERSITY OF MICHIGAN THE REGENTS OF THE UNIVERSITY OF MICHIGAN United States
- Peking University China (People's Republic of)
- Pekin University China (People's Republic of)
- Peking University China (People's Republic of)
Statistics and Probability, FOS: Computer and information sciences, Causal Inference, Artificial intelligence, Genetic variants, Methods for Causal Inference in Observational Studies, Genotype, Science, Sample size determination, transportability, Biochemistry, Gene, Data science, FOS: Economics and business, Methodology (stat.ME), Inference, Randomized experiment, Biochemistry, Genetics and Molecular Biology, Observational study, Machine learning, FOS: Mathematics, Genetics, Mendelian randomization, Econometrics, causal inference, data integration, generalizability, Data mining, Statistics - Methodology, Multiple Testing, data fusion, Chromatography, Sample (material), Statistics, Life Sciences, Computer science, Statistics and Numeric Data, Genomic Studies and Association Analyses, Chemistry, FOS: Biological sciences, Physical Sciences, Overviews, Computational methods for problems pertaining to statistics, Mathematics, Statistical Methods in Clinical Trials and Drug Development, Causal inference
Statistics and Probability, FOS: Computer and information sciences, Causal Inference, Artificial intelligence, Genetic variants, Methods for Causal Inference in Observational Studies, Genotype, Science, Sample size determination, transportability, Biochemistry, Gene, Data science, FOS: Economics and business, Methodology (stat.ME), Inference, Randomized experiment, Biochemistry, Genetics and Molecular Biology, Observational study, Machine learning, FOS: Mathematics, Genetics, Mendelian randomization, Econometrics, causal inference, data integration, generalizability, Data mining, Statistics - Methodology, Multiple Testing, data fusion, Chromatography, Sample (material), Statistics, Life Sciences, Computer science, Statistics and Numeric Data, Genomic Studies and Association Analyses, Chemistry, FOS: Biological sciences, Physical Sciences, Overviews, Computational methods for problems pertaining to statistics, Mathematics, Statistical Methods in Clinical Trials and Drug Development, Causal inference
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