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On an intuitive level, the idea is that the factorization of the joint distribution P(Cause, Effect) into  P(Cause)*P(Effect | Cause) typically yields models of lower total complexity than the factorization into  P(Effect)*P(Cause | Effect). Although the notion of “complexity” is intuitively appealing, it is not obvious how it should be precisely defined.<ref name="Mooij"/> A different family of methods attempt to discover causal "footprints" from large amounts of labeled data, and allow the prediction of more flexible causal relations.<ref>Lopez-Paz, David, et al. "[http://www.jmlr.org/proceedings/papers/v37/lopez-paz15.pdf Towards a learning theory of cause-effect inference]" ICML. 2015</ref>

On an intuitive level, the idea is that the factorization of the joint distribution P(Cause, Effect) into  P(Cause)*P(Effect | Cause) typically yields models of lower total complexity than the factorization into  P(Effect)*P(Cause | Effect). Although the notion of “complexity” is intuitively appealing, it is not obvious how it should be precisely defined.<ref name="Mooij"/> A different family of methods attempt to discover causal "footprints" from large amounts of labeled data, and allow the prediction of more flexible causal relations.<ref>Lopez-Paz, David, et al. "[http://www.jmlr.org/proceedings/papers/v37/lopez-paz15.pdf Towards a learning theory of cause-effect inference]" ICML. 2015</ref>

在直观的层面上，这个想法是联合分布P(Cause, Effect) 到 P(Cause)*P(Effect | Cause)的因式分解通常产生的模型的总'''<font color=’#ff8000>复杂性complexity </font>'''低于到P(Effect)*P(Cause | Effect)的因式分解。尽管“复杂性”的概念在直觉上很吸引人，但是对于如何定义它却并不显而易见。另一种不同类族的方法尝试从大量标签过的数据中发现因果的“足迹”，并且允许预测更灵活的因果关系。

在直观的层面上，这个想法是联合分布P(Cause, Effect) 到 P(Cause)*P(Effect | Cause)的因式分解通常产生的模型的总'''<font color='#ff8000'>复杂性complexity </font>'''低于到P(Effect)*P(Cause | Effect)的因式分解。尽管“复杂性”的概念在直觉上很吸引人，但是对于如何定义它却并不显而易见。另一种不同类族的方法尝试从大量标签过的数据中发现因果的“足迹”，并且允许预测更灵活的因果关系。

== In statistics and economics ==

== In statistics and economics ==