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arXiv 提交日期: 2026-05-04
📄 Abstract - The Causal Description Gap: Information-Theoretic Separations Across Pearl's Hierarchy

Pearl's causal hierarchy shows that observational, interventional, and counterfactual queries are qualitatively distinct. We ask a quantitative version of this question: how many additional bits are needed to specify higher-rung causal answers once lower-rung answers are known? We formalize this via query-class description length, the Kolmogorov complexity of the answer oracle induced by an SCM for a class of queries. Our main construction gives binary acyclic SCMs whose observational distribution has constant description length, while the single-variable interventional answer oracle has description length $\Theta(n^2)$. A degree-sensitive upper bound shows that finite-gate-schema SCMs of indegree $d$ have observational-interventional gap at most $O(nd \log(en/d) + n \log n)$, making the quadratic construction order-optimal in the dense regime and a rooted-tree construction order-optimal for bounded indegree. The quadratic separation persists under $\varepsilon$-accurate total-variation descriptions for every fixed $\varepsilon < 1/4$. At the next rung, the full hard-do interventional oracle can still leave a $\Theta(n)$ counterfactual description gap. A general ambiguity-to-bits theorem and Shannon analogue show that these gaps equal the logarithm of residual higher-rung ambiguity up to lower-order terms.

顶级标签: theory machine learning
详细标签: causal hierarchy kolmogorov complexity interventional queries counterfactual reasoning description length 或 搜索:

因果描述差距:珀尔层级中的信息论分离 / The Causal Description Gap: Information-Theoretic Separations Across Pearl's Hierarchy


1️⃣ 一句话总结

本文通过信息论方法量化了因果层级(观察、干预、反事实)之间的描述复杂度差距,证明在最简单二元因果模型中,仅需常数位描述观察分布,而单变量干预答案却需要平方级位数,揭示了不同因果层级在信息表达上的本质鸿沟。

源自 arXiv: 2605.02177