The paper formalizes algorithmic causality as causal statements read off from the Turing machine that best compresses multi-environment data, and proves that minimal finite codebook complexity favors sparse mechanism shifts and invariant factorizations.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Algorithmic causal structure emerging through compression
The paper formalizes algorithmic causality as causal statements read off from the Turing machine that best compresses multi-environment data, and proves that minimal finite codebook complexity favors sparse mechanism shifts and invariant factorizations.