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To determine whether a causal relation is identified from an arbitrary Bayesian network with unobserved variables, one can use the three rules of "''do''-calculus"<ref name="pearl2000"/><ref name="pearl-r212">{{cite conference |url=http://dl.acm.org/ft_gateway.cfm?id=2074452&ftid=1062250&dwn=1&CFID=161588115&CFTOKEN=10243006 |title=A Probabilistic Calculus of Actions | vauthors = Pearl J |year=1994 | veditors = Lopez de Mantaras R, Poole D | booktitle = UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence |publisher=[[Morgan Kaufmann]] |location=San Mateo CA |pages=454–462 |isbn=1-55860-332-8 |arxiv=1302.6835 |bibcode=2013arXiv1302.6835P }}</ref> and test whether all ''do'' terms can be removed from the expression of that relation, thus confirming that the desired quantity is estimable from frequency data.<ref>{{cite book | vauthors = Shpitser I, Pearl J | chapter = Identification of Conditional Interventional Distributions | veditors = Dechter R, Richardson TS | title  = Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence | pages = 437–444 | location = Corvallis, OR | publisher = AUAI Press | year = 2006 | arxiv = 1206.6876 }}</ref>

To determine whether a causal relation is identified from an arbitrary Bayesian network with unobserved variables, one can use the three rules of "''do''-calculus"and test whether all ''do'' terms can be removed from the expression of that relation, thus confirming that the desired quantity is estimable from frequency data.

To determine whether a causal relation is identified from an arbitrary Bayesian network with unobserved variables, one can use the three rules of "do-calculus" and test whether all do terms can be removed from the expression of that relation, thus confirming that the desired quantity is estimable from frequency data.

To determine whether a causal relation is identified from an arbitrary Bayesian network with unobserved variables, one can use the three rules of "do-calculus" and test whether all do terms can be removed from the expression of that relation, thus confirming that the desired quantity is estimable from frequency data.

为了确定一个因果关系是否可以从一个含有未观测变量的贝叶斯网络中识别出来，我们可以使用“ do-演算”的三个规则来检验是否所有的 do 项都可以从这个关系的表达式中去掉，从而确认所需的量可以从数据中估计出来。

为了确定一个因果关系是否可以从一个含有未观测变量的贝叶斯网络中识别出来，我们可以使用“ do-演算”<ref name="pearl2000"/><ref name="pearl-r212">{{cite conference |url=http://dl.acm.org/ft_gateway.cfm?id=2074452&ftid=1062250&dwn=1&CFID=161588115&CFTOKEN=10243006 |title=A Probabilistic Calculus of Actions | vauthors = Pearl J |year=1994 | veditors = Lopez de Mantaras R, Poole D | booktitle = UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence |publisher=[[Morgan Kaufmann]] |location=San Mateo CA |pages=454–462 |isbn=1-55860-332-8 |arxiv=1302.6835 |bibcode=2013arXiv1302.6835P }}</ref> 的三个规则来检验是否所有的 do 项都可以从这个关系的表达式中去掉，从而确认所需的量可以从数据中估计出来。<ref>{{cite book | vauthors = Shpitser I, Pearl J | chapter = Identification of Conditional Interventional Distributions | veditors = Dechter R, Richardson TS | title  = Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence | pages = 437–444 | location = Corvallis, OR | publisher = AUAI Press | year = 2006 | arxiv = 1206.6876 }}</ref>