In Causal Discovery with latent variables, We define two data paradigms:
definite data: a single-skeleton structure with observed nodes single-value,
and indefinite data: a set of multi-skeleton structures with observed nodes
multi-value. Multi,skeletons induce low sample utilization and multi values
induce incapability of the distribution assumption, both leading that
recovering causal relations from indefinite data is, as of yet, largely
unexplored. We design the causal strength variational model to settle down
these two problems. Specifically, we leverage the causal strength instead of
independent noise as latent variable to mediate evidence lower bound. By this
design ethos, The causal strength of different skeletons is regarded as a
distribution and can be expressed as a single-valued causal graph matrix.
Moreover, considering the latent confounders, we disentangle the causal graph G
into two relatisubgraphs O and C. O contains pure relations between observed
nodes, while C represents the relations from latent variables to observed
nodes. We summarize the above designs as Confounding Disentanglement Causal
Discovery (biCD), which is tailored to learn causal representation from
indefinite data under the latent confounding. Finally, we conduct comprehensive
experiments on synthetic and real-world data to demonstrate the effectiveness
of our method.

本文提出了一种称作混淆分离因果发现算法（Confounding Disentanglement Causal Discovery，简称 biCD）的方法，通过使用因果强度变分模型，将潜在变量作为中介变量来解决存在潜在变量的非确定数据情形下的因果关系发现问题。我们将结果用合成和实际数据进行了验证，证明了该方法的有效性。