In his 1984 paper "Statistics and Causal Inference", Paul Holland raised one of the most fundamental questions in statistics:
What can a statistical model say about causation?
This led to his motto:
NO CAUSATION WITHOUT MANIPULATION
which emphasized the importance of restrictions around experiments that consider causation. Andrew Gelman makes a similar point:
"To find out what happens when you change something, it is necessary to change it."...There are things you learn from perturbing a system that you'll never find out from any amount of passive observation.
His ideas are summarized in this article.
What considerations should be made when making a causal inference from a statistical model?