Consumer Data Research Centre
 

The CDRC causal inference summer school is back!

Applications are now open for the much-anticipated Consumer Data Research Centre (CDRC) summer school Causal Inference with Observational Data: challenges and pitfalls (9th-13th July 2018). The school ran in 2017 to huge acclaim from participants, and returns this year with even more content and teaching as it is now a 5 day as opposed to 4 day school.

Taking place at the Leeds Institute for Data Analytics (LIDA), the summer school comprises state-of-the-art training in the analysis of observational data for causal inference. By exploring the philosophy and utility of directed acyclic graphs (DAGs), participants will learn to recognise and avoid a range of common pitfalls in the analysis of complex causal relationships, including the longitudinal analyses of change, mediation, nonlinearity and statistical interaction.

Specifically, the course will cover the following:

  • Prediction vs causal inference
  • Advanced use of directed acyclic graphs (DAGs);
  • The role and relevance of covariates in multiple regression;
  • Collider bias in sample selection (including reversal paradox and the Table 2 Fallacy)
  • Conditioning on the outcome (including regression to the mean);
  • Compositional data errors (including mathematical coupling and composite variable bias)
  • Analysis of change and statistical evaluation of longitudinal data
  • Statistical interaction and model parameterisation issues.
  • Time-varying exposures, time-varying confounding, and G-methods

The key aim of the summer school is to introduce the thinking into statistical modelling of observational data, and to debunk some common misconceptions around causal inference using DAGs, and to teach participants how to avoid the common pitfalls of using observational data. Although the training is delivered by three professionals from the School of Medicine, Prof Mark Gilthorpe, Dr Peter Tennant and Dr George Ellison, applications are not limited to those with a research background in health or medical sciences but are open to all social science disciplines.

Last year’s participants gave glowing feedback on the course, saying that it was well structured, and a game-changer in terms of their thought-processes when they approach their research now. Below are some of the comments following the 2017 summer school:

 

“The course was a paradigm shift for me in terms of thinking around causal inference and gave me the tools to think about some important pitfalls in analysis that I would have otherwise missed.”

“I found the discussion of how to construct DAGs and the different causes of bias (e.g. regression to the mean, numerical coupling etc.) the most interesting.”

“It was great to think about my own research in a different, more critical way.”

“The tutors were all extremely knowledgeable, approachable, and oozed enthusiasm for the subject!”

“I would not have had any formal training in causal analysis if it were not for this course, it has made me aware of many issues which I will now be alerted to.”

“Now I’ll think more causally about my analysis and hypotheses, and will definitely use things like DAGs to clarify my thinking and analysis strategy.”

 

Fees and how you can apply

Full details of the summer school can be found here.

Fees are as follows:

£295 (postgraduate students)*

£595 (researchers, academics, public and charitable sector)*

Places for the summer school are limited to 25 so that tutors have good contact time with each participant. These places are expected to fill fast, so apply now using this application form to avoid disappointment. Your application will be reviewed within 1 week and, if you are successful, we will then send you a booking link to pay for your place.

 

*Fees include tuition, refreshments and lunch for the 5 days; accommodation, breakfast and travel are not included.

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