Causal Inference for Health and Social Scientists

This is course is co-organized with


26-30 August 2024


9:30 to 13:00 CEST


In person

Intended for

Researchers who work with large databases of individual-level data, health policy practitioners.


Working knowledge of study design and regression analysis, interest in evaluation of policy interventions for public health and medicine.


The course introduces a general-purpose causal inference framework that integrates methods for both experimental and non-experimental data. The framework has two steps: 1) specification of the (hypothetical) target experiment or trial that would answer the causal question of interest, and 2) emulation of the target trial using the available data. The course explores key challenges for target trial emulation and critically reviews methods proposed to overcome those challenges. The methods are presented in the context of the evaluation of the comparative effectiveness of health interventions using existing databases of administrative and clinical data. At the end of the course students should be able to:

  • Formulate sufficiently well-defined causal questions
  • Specify the protocol of the target trial
  • Design analyses of observational data that emulate the target trial
  • Identify key assumptions for a correct emulation of the target trial


  • Causal inference as a key component of decision making
  • Target trial emulation as a unifying concept for causal inference
  • Target trial emulation to avoid self-inflicted biases in causal inference
  • Point interventions vs. sustained policies
  • G-methods to evaluate sustained policies

Miguel Hernán conducts research to learn what works to improve human health. He is the Director of the CAUSALab at the Harvard T.H. Chan School of Public Health, where he and his collaborators design analyses of health databases, epidemiologic studies, and randomized trials. He is the co-director of the Laboratory for Early Psychosis (LEAP) Center, principal investigator of the HIV-CAUSAL Collaboration, and co-director of the VA-CAUSAL Methods Core, an initiative of the U.S. Veterans Health Administration to integrate high-quality data and explicitly causal methodologies in a nationwide learning health system. As Kolokotrones Professor of Biostatistics and Epidemiology, he teaches causal inference methodology at the Harvard Chan School and clinical epidemiology at the Harvard-MIT Division of Health Sciences and Technology. His edX course "Causal Diagrams" and his book "Causal Inference: What If", co-authored with James Robins, are freely available online and widely used for the training of researchers. Miguel is an elected Fellow of the American Association for the Advancement of Science and of the American Statistical Association, Editor Emeritus of Epidemiology, and past Associate Editor of Biometrics, American Journal of Epidemiology, and the Journal of the American Statistical Association.