The common cause principle for two random variables $A$ and $B$ is examined
in the case of causal insufficiency, when their common cause $C$ is known to
exist, but only the joint probability of $A$ and $B$ is observed. As a result,
$C$ cannot be uniquely identified (the latent confounder problem). We show that
the generalized maximum likelihood method can be applied to this situation and
allows identification of $C$ that is consistent with the common cause
principle. It closely relates to the maximum entropy principle. Investigation
of the two binary symmetric variables reveals a non-analytic behavior of
conditional probabilities reminiscent of a second-order phase transition. This
occurs during the transition from correlation to anti-correlation in the
observed probability distribution. The relation between the generalized
likelihood approach and alternative methods, such as predictive likelihood and
the minimum common cause entropy, is discussed. The consideration of the common
cause for three observed variables (and one hidden cause) uncovers causal
structures that defy representation through directed acyclic graphs with the
Markov condition.

本文介绍了基于广义最大似然方法的共同原因原理，研究了在因果不充分的情况下，如何处理共同原因 $C$ 无法被唯一确定的问题，并讨论了共同原因对受限制的三个变量之间的因果结构的影响。