We develop a necessary and sufficient causal identification criterion for
maximally oriented partially directed acyclic graphs (MPDAGs). MPDAGs as a
class of graphs include directed acyclic graphs (DAGs), completed partially
directed acyclic graphs (CPDAGs), and CPDAGs with added background knowledge.
As such, they represent the type of graph that can be learned from
observational data and background knowledge under the assumption of no latent
variables. Our identification criterion can be seen as a generalization of the
g-formula of Robins (1986). We further obtain a generalization of the truncated
factorization formula (Pearl, 2009) and compare our criterion to the
generalized adjustment criterion of Perkovi\'c et al. (2017) which is
sufficient, but not necessary for causal identification.

我们为最大定向部分有向无环图（MPDAGs）开发了必要且充分的因果识别标准，该标准可被视为 Robins（1986）g - 公式的推广。我们进一步获得了截断因式分解公式（Pearl，2009）的推广，并将我们的标准与 Perković等人的广义调整标准（2017）进行了比较，后者对于因果识别是充分的，但不是必要的。