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Time & Location
Apr 24, 2023, 9:30 AM GMT+1
About the event
This one-day intermediate workshop will:
- Introduce the basic principles of causal modelling (potential outcomes, graphs, causal effects) while emphasising the key role of design and assumptions in obtaining robust estimates.
- Introduce the basic principles of machine learning and the use of machine learning methods to do causal inference (e.g. methods stemming from domain adaptation and propensity scores).
- Show how to implement these techniques for causal analysis and interpret the results in illustrative examples.
The course covers:
- Causal modelling
- Basic machine learning techniques
- Running causal analysis on real data sets
By the end of the course participants will:
- Understand the distinction between causal effects and associations and appreciate the key role of design and possibly untestable assumptions in the estimation of causal effects
- Understand the role of training and testing models on data and the use of regularization to avoid overfitting
- Be able to position machine learning within the causal tool chain
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