8 Feb 2022 01:30pm to 11 Feb 2022 05:00pm

Introduction to Causal Inference (Summer School 2022 days 1-4)

Workshop
Event Location
Australia
Speakers
Margarita is co-lead of the Clinical Epidemiology and Biostatistics Unit (CEBU) at the MCRI and the University of Melbourne. Since completing her PhD in Biostatistics at Université Paris-Sud in 2014...
Prof John Carlin
John holds appointments with the Murdoch Children’s Research Institute and The University of Melbourne. Since completing a PhD in Statistics at Harvard University he has been engaged as a collaborator...
Andrew is the current Head of the Biostatistics Unit in the Department of Epidemiology and Preventive Medicine at Monash University. Since completing a PhD in Statistics at Cornell University (USA)...
Lyle Gurrin is Professor of Biostatistics at the Centre for Epidemiology and Biostatistics at the Melbourne School of Population and Global Health, and President of the Victorian Branch of the...
Jessica completed a PhD at the University of Adelaide in 2010. Her research interests include statistical methods for the comparison of healthcare provider performance and assessment of changes in performance,...
Marnie is a biostatistician at the Murdoch Children's Research Institute. She completed her PhD at The University of Melbourne in 2020, investigating the application of multilevel regression and poststratification for...
Daisy completed her PhD at the University of Auckland in 2019, researching and developing goodness-of-fit methods for phylogenetic models. Her current research focuses on modern approaches to causal inference, with...
Ghazaleh Dashti
Ghazaleh completed her PhD in Epidemiology at the University of Melbourne in 2020. Her current research focuses on methods for handling missing data in the context of causal inference methods...
Koen Simons
Dr Koen Simons is currently working for University of Melbourne as a lecturer. He completed his PhD at the Vrije Universiteit Brussel in 2016. He has a preference for free/open...
Susie is undertaking a PhD in data-adaptive methods for causal inference using machine learning.
Jiaxin Zhang
Topic : Development and evaluating of causal inference methods for epidemiological studies with missing data

In this era of “data science” it is vitally important to clearly articulate the questions that we ask of data, understand the challenges inherent in answering different types of questions, and ensure that our analysis methods are suitably aligned. An overwhelming number of clinical and public health research studies ask “causal” questions, for example about the effect of treatments, policies, behaviours and other exposures on health outcomes. These questions are key to guiding decision making in health policy and practice when the goal is to improve patient outcomes and population health. Causal inference requires carefully structured reasoning to guide appropriate statistical analysis. This 2-part workshop will provide a comprehensive introduction to causal thinking and the key methods for defining and estimating causal effects.

N.B. Registrations are available for Part 1 only or for Parts 1 and 2.

Part 2 only registration will only be available if you have taken the course “Observational studies: Modern concepts & analytic methods” delivered by the Clinical Epidemiology and Biostatistics Unit (CEBU) at the Melbourne Children’s campus. (Part 1 is identical to the first half of that course). Please email to confirm this prior to registering.

 

 

Part 1 – Study Design

February 8 & 9 1:30-5pm

This course introduces key causal inference concepts and outlines an approach for designing causal analyses of observational studies. Specifically, we will examine what is meant by causal inference and how to identify causal questions, followed by an introduction to the target trial approach for defining causal effects. We introduce directed acyclic graphs (DAGs) and show how they can be used to understand potential biases in emulating the target trial. We then bring it all together, demonstrating an approach to planning causal analyses.

The course includes lectures followed by tutorials that develop understanding of the concepts. All lectures and tutorials include illustrations from real-world observational epidemiological studies. Electronic copies of presentation materials will be made available online.

Prerequisites:  It is assumed that students will have a background including elementary statistical concepts, such as population and sample, and standard methods for simple analyses (mean difference, chi-squared test etc).

 

Registration closes 5pm Friday 4th February

Register here

 

Part 2 – Analysis Methods

February 10 & 11 1:30-5pm

This course introduces three key methods for causal effect estimation: standard regression, g-computation and inverse probability weighting (IPW).

Lectures and tutorials will help ground understanding of the methods, underlying intuition, and assumptions, whilst a hands-on computer practical (in R and Stata) will cover their practical implementation. All lectures and tutorials include illustrations from real-world observational epidemiological studies. Electronic copies of presentation materials will be made available online.

Prerequisites:  Students must have done Part 1 of the course, either during this Summer School or through the aforementioned CEBU course at the Children’s/MCRI. To do the computer practical, students must also have a sound working familiarity with Stata or R and have the corresponding software installed on their computer or laptop.

 

Registration closes 5pm Tuesday 8th February

Register here