Causal DAG extraction from a library of books or videos/movies
This addresses a major bottleneck in causal inference for statistics, AI, and ML, though it appears incremental as it builds on existing ideas with a new application.
The paper tackles the challenge of constructing causal directed acyclic graphs (DAGs) for causal inference by proposing an algorithm to build an atlas of DAGs from books or videos, and demonstrates it on a Tic-Tac-Toe game database with open-source software.
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.