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

TL;DR

This paper introduces a method to construct causal DAG atlases from libraries of books or videos/movies, aiding causal inference in AI and ML by mimicking human-like causal reasoning.

Contribution

It proposes a simple algorithm to build causal DAG atlases from multimedia libraries, bridging a gap in causal inference methods.

Findings

Successfully applied to a database of Tic-Tac-Toe games

Open source software available on GitHub

Demonstrates potential for causal reasoning from multimedia collections

Abstract

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.