Causal Discovery using Compression-Complexity Measures

Understand

Causal inference is one of the most fundamental problems across all domains of science.

• We address the problem of inferring a causal direction from two observed discrete symbolic sequences $X$ and $Y$.
• We present a framework which relies on lossless compressors for inferring context-free grammars (CFGs) from sequence pairs and quantifies the extent to which the grammar inferred from one sequence compresses the other sequence.
• We infer $X$ causes $Y$ if the grammar inferred from $X$ better compresses $Y$ than in the other direction.