Data integration in causal inference - Shi, Xu; Pan, Ziyang; Miao, Wang (Wiley interdisciplinary reviews. Computational statistics, 2023)
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 Data missing patterns in the major settings discussed in Sections 3 and 4. For each variable in each sample, ✓ stands for observed, empty stands for unobserved, and ✓/✗ indicates different settings considered by different papers.