INTRODUCTION TO CAUSAL INFERENCE COURSE
Our introduction to causal inference course for health and social scientists offers a friendly and accessible training in the theory and practice of analysing observational data for causal inference. Delivered in-person over five-days by a team of experts, the course provides an unmissable introduction to this exciting and evolving aspect of contemporary data science.
COURSE CONTENT
- The needs for, and principles of, quantitative causal inference
- Counterfactuals, potential outcomes, and the Potential Outcomes Framework
- Distinguishing (associative) prediction and causal inference
- Introduction to causal directed acyclic graphs (DAGs)
- How to draw and evaluate DAGs to aid with identifying and estimating causal effects in health and social science contexts
- Covariate roles, the Table 2 Fallacy, and relevance for algorthmic interpretability
- Classical confounding and propensity score approaches
- Introduction to collider bias and selection bias, including Berkon’s paradox, the birthweight paradox, and outcome truncation bias
- Reducing selection bias with inverse probability weighting
- Exposure regimes and causal mediation analysis
- Analysing multiple exposures and reducing time-varying confounding with G-methods
- Including deterministic variables within DAGs to help with estimating and interpreting causal effects involving tautological associations, compositional data, and composite variables
- Instrumental variable approaches, including Mendelian Randomisation
- Threshold and discontinuity approaches, including difference-in-differences, interrupted time series analyses, and regression discontinuity designs
FURTHER DETAILS
FORMAT
Our introduction to causal inference course is a five-day in-person training event. It is not possible to attend remotely.
The course includes of a mix of face-to-face lectures, interactive activities, and question and discussion sessions.
AUDIENCE
Our introduction to causal inference course is designed for applied health and social scientists.
Although we aim to make the material as accessible as possible, we recommend a working knowledge of linear regression modelling.
DATE & VENUE
The next introduction to causal inference course will be held during 15th-19th May 2023.
The course will be held at the St George’s Centre, located within the centre of Leeds, UK. Catering will be provided by a local charity that provides food for homeless people in Leeds.
ATTENDANCE FEE
Our standard fee is £1095, but a reduced fee of £695 is available for current students.
The fee includes attendance, access to all teaching materials, and lunch and refreshments. Please note that the fee does not include accommodation.
OUR TEAM
PETER TENNANT
Peter is an Associate Professor of Health Data Science at the University of Leeds and a co-lead of the Alan Turing Institute’s Causal Inference Interest Group. He is a renowned public speaker and educator, and also internationally recognised for his research to adapt and introduce causal inference methods into applied health and social science research. He established the introduction to causal inference course in 2017.
Laurie Berrie
Laurie is a Post-Doctoral Research Assistant in Health and Wellbeing at the University of Edinburgh. Their PhD research adapting causal inference into geographical research led to seminal works on causal inference with compositional data and composite variables. They have previously taught on the introduction to causal inference course in 2017-2018 and we are thrilled to be welcoming them back for 2023.
Leonardo Gada
Leonardo is a Senior Statistical Officer at the Department for Work and Pensions, where he supports service delivery and is involved in communicating statistics to a wide range of stakeholders. He began working in the civil service after completing an MSc in Health Data Analytics at the University of Leeds, where he first learnt causal inference. We are delighted to be welcoming Leonardo to join our team for the first time in 2023.
GEORGIA TOMOVA
Georgia is a PhD student in Health Data Science at the Alan Turing Intitute and a co-lead of the Alan Turing Institute’s Causal Inference Interest Group. Her research adapting causal inference into nutrition research has attracted international praise and she is repeatedly invited to speak on the topic. She has been teaching on the introduction to causal inference course since 2019.
BOOK YOUR PLACE
Our Introduction to Causal Inference Course in May 2023 is now fully booked!
Our reserve list is now full, but you may express advanced interest in a future course by joining our mailing list using the link below. You will receive priority information on future courses before they are widely advertised.