Miguel Hernan's research focuses on the methodology of causal inference and comparative effectiveness in public health. He emphasizes the importance of making policy and clinical decisions based on findings from randomized experiments, despite the practical limitations of conducting such trials. In particular, he examines how public health recommendations can be informed by comparing the effectiveness of various interventions, even when randomized experiments are unethical or impractical. Hernan collaborates with researchers to combine observational data with statistical methods to emulate hypothetical randomized experiments, striving to formulate clear causal questions and apply valid analytical approaches. He is a strong advocate for recognizing the limitations of observational data while still leveraging it to guide future randomized experiments. His work aims to enhance the framework for health decision-making, ensuring that findings from properly analyzed observational studies can significantly impact policy and clinical guidelines.