Accumulated local effects (ALE) is a machine learning interpretability method.
[1] ALE uses a conditional feature distribution as an input and generates augmented data, creating more realistic data than a marginal distribution.
[2] Given a model that predicts house prices based on its distance from city center and size of the building area, ALE compares the differences of predictions of houses of different sizes.
The result separates the impact of the size from otherwise correlated features.
High correlations between features can defeat the technique.