  Determining a causal DAG (directed acyclic graph) for a problem under
consideration, is a major roadblock when doing Judea Pearl's Causal Inference
(CI) in Statistics. The same problem arises when doing CI in Artificial
Intelligence (AI) and Machine Learning (ML). As with many problems in Science,
we think Nature has found an effective solution to this problem. We argue that
human and animal brains contain an explicit engine for doing CI, and that such
an engine uses as input an atlas (i.e., collection) of causal DAGs. We propose
a simple algorithm for constructing such an atlas from a library of books or
videos/movies. We illustrate our method by applying it to a database of
randomly generated Tic-Tac-Toe games. The software used to generate this
Tic-Tac-Toe example is open source and available at GitHub.
