ABSTRACT
Linear models can be used for prediction or explanation. Prediction is not simple but it is conceptually easier than explanation. We have been deliberately vague about the meaning of explanation. Sometimes explanation means causation but sometimes it is just a description of the relationships between the variables. Causal conclusions require stronger assumptions than those used for predictive models. This chapter looks at the conditions necessary to conclude a causal relationship and what can be said when we lack these conditions.
