Main content:

1. Causal Discovery: the process of identifying causal relationships from data. This involves uncovering the underlying causal structure without assuming prior knowledge of the direction or nature of causality. It typically uses statistical and computational methods to determine which variables influence which other variables.

2. Causal Inference: the process of estimating the strength and nature of causal relationships that have been identified. It involves using statistical techniques to quantify the effect of one variable on another, given the causal structure is known or assumed.

3. Interpretability: the ability to understand and explain how a model makes its predictions or decisions. It involves techniques that provide insights into the importance of different features, the effect of individual variables, and the overall behaviour of the model.

4. Experiments and Data Collection: the structured and methodical approach to planning, conducting, analysing, and interpreting controlled experiments. It involves techniques for setting up experiments to ensure that the results are valid, reliable, and can be used to draw causal conclusions.