<font color = '#ff8000'>从联合观测数据中确定两个时间无关变量(例如X和Y)的因果关系,利用某些模型在X→Y和Y→X方向上的证据之间的不对称性。Determination of cause and effect from joint observational data for two time-independent variables, say X and Y, has been tackled using asymmetry between evidence for some model in the directions, X → Y and Y → X.</font>主要的方法基于'''<font color = '#ff8000'>算法信息理论 Algorithmic information theory</font>'''模型和'''<font color = '#ff8000'>噪声模型 noise models</font>'''。
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'''<font color='#32cd32'>从联合观测数据中确定两个时间无关变量(例如X和Y)的因果关系,利用某些模型在X→Y和Y→X方向上的证据之间的不对称性。Determination of cause and effect from joint observational data for two time-independent variables, say X and Y, has been tackled using asymmetry between evidence for some model in the directions, X → Y and Y → X.</font>'''主要的方法基于'''<font color = '#ff8000'>算法信息理论 Algorithmic information theory</font>'''模型和'''<font color = '#ff8000'>噪声模型 noise models</font>'''。
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比较两个同时输出 X 和 Y 的程序。
比较两个同时输出 X 和 Y 的程序。
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* 根据未压缩的Y存储Y和X的压缩型 Store Y and a compressed form of X in terms of uncompressed Y.
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* '''<font color='#32cd32'>根据未压缩的Y存储Y和X的压缩型 Store Y and a compressed form of X in terms of uncompressed Y.</font>'''
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* 根据未压缩的X存储X和Y的压缩型 Store X and a compressed form of Y in terms of uncompressed X.
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* '''<font color='#32cd32'>根据未压缩的X存储X和Y的压缩型 Store X and a compressed form of Y in terms of uncompressed X.</font>'''
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最短的此类程序表明,未压缩的'''<font color='#ff8000>存储变量stored variable</font>'''更有可能导致'''<font color='#ff8000>计算变量 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>
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'''<font color='#32cd32'>最短的此类程序表明,未压缩的存储变量stored variable更有可能导致计算变量 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>