1 Introduction to Causality
This chapter covers
- Why and when we need causal inference
- How causal inference works
- Understanding the difference between observational data and experimental data
- Reviewing relevant statistical concepts
In most of the machine learning applications you find in commercial enterprises (and outside research), your objective is to make predictions. So, you create a predictive model that, with some accuracy, will make a guess about the future. For instance, a hospital may be interested in predicting which patients are going to be severely ill, so that they can prioritize their treatment. In most predictive models, the mere prediction will do; you don’t need to know why it is the way it is.
Causal inference works the other way around. You want to understand why, and moreover you wonder what could we do to have a different outcome. A hospital, for instance, may be interested in the factors that affect some illness. Knowing these factors will help them to create public healthcare policies or drugs to prevent people from getting ill. The hospital wants to change how things currently are, in order to reduce the number of people ending up in the hospital.