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====Causal representation learning====

====Causal representation learning====

[[Causal representation learning]] is an emerging field in artificial intelligence. It attempts to combine two important fields in machine learning: [[representation learning]] and [[causal inference]]. By integrating their respective advantages, it aims to automatically extract important features and uncover causal relationships hidden within the data <ref>B. Sch ̈olkopf, F. Locatello, S. Bauer, N. R. Ke, N. Kalchbrenner, A. Goyal, Y. Bengio, Toward causal representation learning, Proceedings of the IEEE 109 (5) (2021) 612–634.</ref>.Causal emergence identification based on EI can be equivalent to a causal representation learning task. Identifying the emergence of causal relationships from data is equivalent to learning the underlying causal relationships and causal mechanisms behind the data. Specifically, we can regard the macroscopic state as a causal variable and the macroscopic dynamics as a causal mechanism by analogy. The coarse-graining strategy can be seen as an encoding process or representation from the original data to the causal variable, while the EI serves as a measure of the strength of causal effect of the mechanism.

[[Causal representation learning]] is an emerging field in artificial intelligence. It attempts to combine two important fields in machine learning: [[representation learning]] and [[causal inference]]. By integrating their respective advantages, it aims to automatically extract important features and uncover causal relationships hidden within the data <ref>B. Sch ̈olkopf, F. Locatello, S. Bauer, N. R. Ke, N. Kalchbrenner, A. Goyal, Y. Bengio, Toward causal representation learning, Proceedings of the IEEE 109 (5) (2021) 612–634.</ref>. Causal emergence identification based on EI can be equivalent to a causal representation learning task. Identifying the emergence of causal relationships from data is equivalent to learning the underlying causal relationships and causal mechanisms behind the data. Specifically, we can regard the macroscopic state as a causal variable and the macroscopic dynamics as a causal mechanism by analogy. The coarse-graining strategy can be seen as an encoding process or representation from the original data to the causal variable, while the EI serves as a measure of the strength of causal effect of the mechanism.

Since there are many similarities between the two, the techniques and concepts of the two fields can learn from each other. For example, [[causal representation learning]] technology can be applied to [[causal emergence identification]]. In turn, the learned abstract causal representation can be interpreted as a macroscopic state, thereby enhancing the interpretability of causal representation learning. However, there are also significant differences between the two, mainly including two points: 1) Causal representation learning assumes that there is a real [[causal mechanism]] behind it, and the data is generated by this causal mechanism. However, there may not be a "true causal relationship" between the emergent macroscopic states and dynamics; 2) The macroscopic state after coarse-graining in causal emergence is a low-dimensional description, but there is no such requirement in causal representation learning. From an epistemological perspective, there is no difference between the two, because what both do is to extract EI from observational data, so as to obtain a representation with stronger causal effect.

Since there are many similarities between the two, the techniques and concepts of the two fields can learn from each other. For example, [[causal representation learning]] technology can be applied to [[causal emergence identification]]. In turn, the learned abstract causal representation can be interpreted as a macroscopic state, thereby enhancing the interpretability of causal representation learning. However, there are also significant differences between the two, mainly including two points: 1) Causal representation learning assumes that there is a real [[causal mechanism]] behind it, and the data is generated by this causal mechanism. However, there may not be a "true causal relationship" between the emergent macroscopic states and dynamics; 2) The macroscopic state after coarse-graining in causal emergence is a low-dimensional description, but there is no such requirement in causal representation learning. From an epistemological perspective, there is no difference between the two, because what both do is to extract EI from observational data, so as to obtain a representation with stronger causal effect.