更改

* '''通过未压缩的Y存储Y和X的压缩型 Store Y and a compressed form of X in terms of uncompressed Y.'''

* '''通过未压缩的Y存储Y和X的压缩型 Store Y and a compressed form of X in terms of uncompressed Y.'''

* '''<font color='#32cd32'>通过未压缩的X存储X和Y的压缩型 Store X and a compressed form of Y in terms of uncompressed X.</font>'''

* '''通过未压缩的X存储X和Y的压缩型 Store X and a compressed form of Y in terms of uncompressed X.'''

'''<font color='#32cd32'>最短的此类程序表明，未压缩的存储变量更有可能导致计算变量。The shortest such program implies the uncompressed stored variable more-likely causes the computed variable.</font>'''<ref>Kailash Budhathoki and Jilles Vreeken "[http://eda.mmci.uni-saarland.de/pubs/2016/origo-budhathoki,vreeken.pdf Causal Inference by Compression]" 2016 IEEE 16th International Conference on Data Mining (ICDM)</ref><ref>{{Cite journal |doi = 10.1007/s10115-018-1286-7|title = Telling cause from effect by local and global regression|journal = Knowledge and Information Systems|year = 2018|last1 = Marx|first1 = Alexander|last2 = Vreeken|first2 = Jilles|volume=60|issue = 3|pages=1277–1305|doi-access = free}}</ref>

'''最短的此类程序表明，未压缩的存储变量更有可能导致计算变量。The shortest such program implies the uncompressed stored variable more-likely causes the computed variable.</font>'''<ref>Kailash Budhathoki and Jilles Vreeken "[http://eda.mmci.uni-saarland.de/pubs/2016/origo-budhathoki,vreeken.pdf Causal Inference by Compression]" 2016 IEEE 16th International Conference on Data Mining (ICDM)</ref><ref>{{Cite journal |doi = 10.1007/s10115-018-1286-7|title = Telling cause from effect by local and global regression|journal = Knowledge and Information Systems|year = 2018|last1 = Marx|first1 = Alexander|last2 = Vreeken|first2 = Jilles|volume=60|issue = 3|pages=1277–1305|doi-access = free}}</ref>

 

===噪音模型 ===

===噪音模型 ===