Courses
23 April: "Causal inference for newcomers" (10:00 to 17:00 CEST)
Rhian Daniel (Cardiff University, UK)
This course is a whistle-stop tour of the key concepts and methods in causal inference, suitable for newcomers to the area. The emphasis throughout is more strongly on the concepts than the methods, with the aim of giving participants enough familiarity with the topics that they are able to follow the majority of the talks in the main EuroCIM meeting. The course will touch on the following:
More information can be found HERE.
This virtual course will be hosted by the University of Florence
Rhian Daniel (Cardiff University, UK)
This course is a whistle-stop tour of the key concepts and methods in causal inference, suitable for newcomers to the area. The emphasis throughout is more strongly on the concepts than the methods, with the aim of giving participants enough familiarity with the topics that they are able to follow the majority of the talks in the main EuroCIM meeting. The course will touch on the following:
- Causal language (do-notation, potential outcomes, counterfactuals)
- Identification, and assumptions that make identification possible (conditional exchangeability / no unmeasured confounding, consistency, positivity, no interference)
- Non-parametric and parametric estimation (including the role of traditional regression models in causal inference)
- Graphical methods (DAGs and SWIGs)
- The propensity score and associated methods
- Instrumental variables (including Mendelian randomisation and the regression discontinuity design)
- Longitudinal casual effects and time-dependent confounding
- Mediation analysis
More information can be found HERE.
This virtual course will be hosted by the University of Florence
18 May: "Causal machine learning" (10:00 - 12:00; 14:00 - 17:00 CEST)
Stijn Vansteelandt (Ghent University, Belgium)
Evaluating treatment effects using observational data increasingly requires adjustment for high-dimensional set of covariates in order to control confounding. This is the result of a lack of comparability between treated and untreated subjects in possibly many (pre-treatment) factors that are also related to outcome. While such adjustment is routinely achieved via parametric modelling, it is not entirely satisfactory as model misspecification is likely, and even relatively minor misspecifications over the observed data range may induce large bias in the treatment effect estimate. Unsurprisingly, machine learning methods are increasingly being used to assist in this task.
This course will cover the use of machine learning in causal effects estimation. It should also be of interest to researchers who rely on variable selection procedures in their data analyses.
This course will consist of two parts. Participants can register to either parts or both.
Further details, as well as a preliminary time schedule, can be found HERE.
This virtual event will be hosted by the London School of Hygiene and Tropical Medicine