What is the relation between causal inference and prediction?

What are the relationships and the differences between causal inference and prediction (both classification and regression)?

In the prediction context, we have the predictor/input variables and response/output variables. Does that mean that there is causal relation between input and output variables? So, does prediction belong to causal inference?

If I understand correctly, causal inference considers to estimate conditional distribution of one random variable given another random variable, and often use graphical models to represent the conditional independence between random variables. So, causal inference, in this sense, isn't prediction, is it?

• Have you had a look at this Kaggle competition? kaggle.com/c/cause-effect-pairs you might find something interesting Apr 23, 2013 at 0:17
• This paper talks about the difference: Galit Shmueli, To Explain or to Predict?, Statist. Sci. Volume 25, Number 3 (2010), 289-310. Apr 16, 2018 at 22:04

Causal inference is focused on knowing what happens to $$Y$$ when you change $$X$$. Prediction is focused on knowing the next $$Y$$ given $$X$$ (and whatever else you've got).
Usually, in causal inference, you want an unbiased estimate of the effect of $$X$$ on Y. In prediction, you're often more willing to accept a bit of bias if you and reduce the variance of your prediction.