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

Causal DAG extraction from a library of books or videos/movies