Strengthening Association through Causal Inference

Understanding causal association and inference is critical to study health risks, treatment effectiveness, and the impact of health care interventions. Although defining causality has traditionally been limited to rigorous, experimental contexts, techniques to estimate causality from observational data are highly valuable for clinical questions in which randomization may not be feasible or appropriate. In this review, the authors highlight several methodologic options to deduce causality from observational data, including regression discontinuity, interrupted time series, and difference-in-differences approaches. Understanding the potential applications, assumptions, and limitations of quasi-experimental methods for observational data can expand our interpretation of causal relationships for surgical conditions.

Automated summary

Understanding causal association and inference is critical to study health risks, treatment effectiveness, and the impact of health care interventions. This review highlights several methodologic options to deduce causality from observational data.