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[[Causal model abstraction]] belongs to a subfield of artificial intelligence and plays a particularly important role in causal inference and model interpretability. This abstraction can help us better understand the hidden causal mechanisms in the data and the interactions between variables. Causal model abstraction involves optimizing a high-level model to simulate the causal effects of a low-level model <ref>Beckers, Sander, and Joseph Y. Halpern. "Abstracting causal models." Proceedings of the aaai conference on artificial intelligence. Vol. 33. No. 01. 2019.</ref>. A high-level model that generalizes the causal effects of a low-level model is referred to as a causal abstraction of the low-level model.

[[Causal model abstraction]] belongs to a subfield of artificial intelligence and plays a particularly important role in causal inference and model interpretability. This abstraction can help us better understand the hidden causal mechanisms in the data and the interactions between variables. Causal model abstraction involves optimizing a high-level model to simulate the causal effects of a low-level model <ref>Beckers, Sander, and Joseph Y. Halpern. "Abstracting causal models." Proceedings of the aaai conference on artificial intelligence. Vol. 33. No. 01. 2019.</ref>. A high-level model that generalizes the causal effects of a low-level model is referred to as a causal abstraction of the low-level model.

Causal model abstraction examines the interaction between causal relationships and model abstraction, which can be viewed as a coarse-graining process <ref>S. Beckers, F. Eberhardt, J. Y. Halpern, Approximate causal abstractions, in: Uncertainty in artificial intelligence, PMLR, 2020, pp. 606–615.</ref>. Therefore, causal emergence identification and causal model abstraction have many similarities. The original causal mechanism can be understood as microscopic dynamics, and the abstracted mechanism can be understood as macroscopic dynamics. In the [[neural information squeezer]] (NIS), researchers place restrictions on coarse-graining strategies and macroscopic dynamics. They require that the microscopic prediction error of macroscopic dynamics be small enough to exclude trivial solutions. This requirement aligns with causal model abstraction's goal of enrusing the abstracted causal model closely resembles the original model. However, there are also some differences between the two: 1) Causal emergence identification is to coarse-grain states or data, while causal model abstraction is to perform coarse-graining operations on models; 2) Causal model abstraction considers confounding factors, but this point is ignored in the discussion of causal emergence identification.

Causal model abstraction discusses the interaction between causal relationships and model abstraction, which can be viewed as a coarse-graining process <ref>S. Beckers, F. Eberhardt, J. Y. Halpern, Approximate causal abstractions, in: Uncertainty in artificial intelligence, PMLR, 2020, pp. 606–615.</ref>. Therefore, causal emergence identification and causal model abstraction have many similarities. The original causal mechanism can be understood as microscopic dynamics, and the abstracted mechanism can be understood as macroscopic dynamics. In the [[neural information squeezer]] (NIS), researchers place restrictions on coarse-graining strategies and macroscopic dynamics. They require that the microscopic prediction error of macroscopic dynamics be small enough to exclude trivial solutions. This requirement aligns with causal model abstraction's goal of enrusing the abstracted causal model closely resembles the original model. However, there are also some differences between the two: 1) Causal emergence identification is to coarse-grain states or data, while causal model abstraction is to perform coarse-graining operations on models; 2) Causal model abstraction considers confounding factors, but this point is ignored in the discussion of causal emergence identification.

=====Reinforcement learning based on world models=====

=====Reinforcement learning based on world models=====