  Integrating data from multiple heterogeneous sources has become increasingly
popular to achieve a large sample size and diverse study population. This paper
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
trial 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.
