This workshop provides a brief overview of and hands-on experience with causal machine learning (e.g., “causal ML”) methods, with applications in health economics. The workshop includes lectures and practical software demonstrations using R to achieve the following learning objectives:
- Understand benefits and limitations of using ML for predictive purposes versus causal inference
- Understand why and how predictive learning is used
- Familiarise with cross-validation and cross fitting techniques
- Learn how to use causal ML to estimate average treatment effects and conditional average treatment effects
- Implement causal ML methods to examine treatment effect heterogeneity.
The practical sessions require R and related software packages available on CRAN. R is an open source statistical software and it will be used to analyse data available in the public domain. Participants are strongly encouraged to install R on their laptop before the course, as support with installation will not be provided during the course. An instructional document with details about the software installation and required packages will be provided shortly before the course date.
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