In circumstances where randomized controlled trials are not practical or ethical, large-scale data collected from “real-world” clinical settings are fertile ground for the development of statistical methods in comparative effectiveness research. In this framework, causal inference methods are critical to addressing and limiting potential biases. In practice, studies frequently require the comparison of multiple treatments, and patient-centered outcomes of interest are often binary, jointly presenting substantial methodological challenges for causal analyses.
