Module 2: Regression and prediction

Regression, prediction, and other tools are very useful to be applied in machine learning for casual inference.

Regression analysis is about discovering correlations between the _outcome _Y and the regression or features X

When we are interested in a certain effect caused by a particular component of X we can partition the elements of X as follows

X = (D,W')'

Where D is the target regressor and W is called control components

When we are using regression, we want to answer two questions:

1. Prediction: How can we use X to predict Y well?
2. Inference: How does Y change if we change a component of X and we leave the rest of the components fixed?