  The integration of real-world data (RWD) and randomized controlled trials
(RCT) is increasingly important for advancing causal inference in scientific
research. This combination holds great promise for enhancing the efficiency of
causal effect estimation, offering benefits such as reduced trial participant
numbers and expedited drug access for patients. Despite the availability of
numerous data fusion methods, selecting the most appropriate one for a specific
research question remains challenging. This paper systematically reviews and
compares these methods regarding their assumptions, limitations, and
implementation complexities. Through simulations reflecting real-world
scenarios, we identify a prevalent risk-reward trade-off across different
methods. We investigate and interpret this trade-off, providing key insights
into the strengths and weaknesses of various methods; thereby helping
researchers navigate through the application of data fusion for improved causal
inference.
