Causal Inference from pragmatic trials and observational data – Sydney 2019


Course title:

Causal inference from pragmatic trials and observational data


Course overview:

This 3 day course introduces concepts and methods for causal inference from pragmatic trials and observational data. Topics include: an introduction to causal inference, DAGs, causal inference from pragmatic trials and the assessment of comparative effectiveness and safety using routinely collected data in healthcare settings. Upon completion of the course, attendees will be prepared to further explore the causal inference literature and apply the principles to their research.


Course tutors:

  • Professor Miguel Hernán, School of Public Health, Harvard University
  • Dr Eleanor Murray, School of Public Health, Boston University

Scroll down to view full course tutor bios.


Course features:

Getting the most out of pragmatic trials – beyond intention to treat

Dr Eleanor Murray

This 1.5 day course will provide an introduction to the principles of casual inference, including directed acyclic graphs (DAGs). The course will include hands on data analysis using R, SAS, or STATA. Causal inference methods will be applied to the analysis of data from pragmatic clinical trials.

Learning what works from observational data

Prof Miguel Hernán

The 1.5 day course introduces students to a general framework for the assessment of comparative effectiveness and safety, with an emphasis of the use of routinely collected data in healthcare settings. The framework relies on the specification and emulation of a hypothetical randomized trial: the target trial. The course explores key challenges for causal inference and critically reviews methods proposed to overcome those challenges. The methods are presented in the context of several case studies for cancer, cardiovascular, renal, and infectious diseases.

Full programme

Course dates:

Getting the most out of pragmatic trials – beyond intention to treat

Dr Eleanor Murray

Wednesday, 20th November 2019               8:30am – 5pm

Thursday, 21st November 2019                     9am – 12:30pm


Learning what works from observational data

Prof Miguel Hernán

Thursday, 21st November 2019                     1.30pm – 5pm

Friday, 22nd November 2019                          9am – 5pm

Course attendees:

This course is aimed at applied researchers with an interest in causal inference from observational and randomised data. It is relevant for epidemiology, clinical and medical research, social and behavioural sciences, psychology etc.

  • Attendees should have a working knowledge of applied regression analysis. Some prior exposure to the counterfactual approach to causal inference and basic probability theory is not essential.
  • Attendees will require a laptop for both courses.
  • Attendees who are attending ‘Getting the most out of pragmatic trials – beyond intention to treat’ must bring a laptop with a software language installed (either R, SAS or STATA).

Course fee:

Inclusion in course fee:

  • Full course registration
  • Electronic copy of course materials

Full course registration

Dr Eleanor Murray & Prof Miguel Hernan

3 day course





* Please note: 10% GST will be added to course fees. Course fees are in $AuD

**Cancellation Policy – Should your circumstances change and you are no longer able to attend the course, we kindly ask you to contact the course organiser, Anika ( no later than 4 weeks prior to the commencement of the course (Wednesday, 23rd October 2019). A cancellation fee of $100.00 will apply to cover costs incurred in relation to your registration.


Course venue:

John & Betty Lynch Seminar Room

Level 3, Neuroscience Research Australia

Margarete Ainsworth Building

139 Barker Street


Please note that catering is not provided. Belmore Road, Randwick and The Spot, Randwick offer an array of restaurants, cafes and shops and are within a ten/fifteen minute walk from NeuRA. 



Tutor Bios:


Dr Eleanor (Ellie) Murray

Ellie is an assistant professor of epidemiology at Boston University School of Public Health. Her research is on causal inference methodology for improving evidence-based decision-making by patients, clinicians, and policy makers.

She uses novel statistical methods to answer comparative effectiveness questions for complex and time-varying treatments using observational data and randomized trials when available, and individual-level simulation modeling when insufficient data exist in the time frame required for decision-making. She is applying these methods to a variety of medical conditions including HIV progression, cancer, psychiatric conditions, and cardiovascular disease.

She was a postdoctoral research fellow in Epidemiology at the Harvard T.H. Chan School of Public Health, working on causal inference for comparative effectiveness and real-world evidence in the HSPH Program on Causal Inference. She holds an ScD in Epidemiology and MSc in Biostatistics from Harvard, an MPH in Epidemiology from Columbia Mailman School of Public Health, and a BSc in Biology from McGill University. Ellie is the Associate Editor for Social Media at the American Journal of Epidemiology.


Professor Miguel Hernán

Miguel Hernán conducts research to learn what works for the treatment and prevention of cancer, cardiovascular disease, and HIV infection. Together with his collaborators, he designs analyses of healthcare databases, epidemiologic studies, and randomized trials.

Miguel teaches clinical data science at the Harvard Medical School, clinical epidemiology at the Harvard-MIT Division of Health Sciences and Technology, and causal inference methodology at the Harvard T.H. Chan School of Public Health, where he is the Kolokotrones Professor of Biostatistics and Epidemiology.

His edX course Causal Diagrams and his book Causal Inference, 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, an Editor of Epidemiology, and past or current Associate Editor of BiometricsAmerican Journal of Epidemiology, and the Journal of the American Statistical Association.