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

arXiv: https://arxiv.org/abs/2211.00486 · HTML: https://mlsanity.com/paper/2211.00486/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 10 references that are in the map.
- First posted on arXiv 29 Oct 2022.

## Builds on (most cited first, 2 of 2)

- [Review of The Book of Why: The New Science of Cause and Effect](https://mlsanity.com/paper/2003.11635/) (2020)
- [OpBerg: Discovering causal sentences using optimal alignments](https://mlsanity.com/paper/1904.02032/) (2019)

## Abstract (arXiv)

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.