Algorithmic independence of initial condition and dynamical law in thermodynamics and causal inference

Abstract

We postulate a principle stating that the initial condition of a physical system is typically algorithmically independent of the dynamical law. We argue that this links thermodynamics and causal inference. On the one hand, it entails behaviour that is similar to the usual arrow of time. On the other hand, it motivates a statistical asymmetry between cause and effect that has recently postulated in the field of causal inference, namely, that the probability distribution P(cause) contains no information about the conditional distribution P(effect|cause) and vice versa, while P(effect) may contain information about P(cause|effect).

7 pages, latex, 2 figures

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Cited by in corpus (6)

• Causality for Machine Learning
• Describing condensed matter from atomically resolved imaging data: from structure to generative and causal models
• Indistinguishability of causal relations from limited marginals
• Monogamy of Temporal Correlations: Witnessing non-Markovianity Beyond Data Processing
• Entanglement, Complexity, and Causal Asymmetry in Quantum Theories
• Does universal controllability of physical systems prohibit thermodynamic cycles?