Machine Learning for Causal Inference

This one-day intermediate workshop will:

1. 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.
2. 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).
3. 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