  Owing to the cross-pollination between causal discovery and deep learning,
non-statistical data (e.g., images, text, etc.) encounters significant
conflicts in terms of properties and methods with traditional causal data. To
unify these data types of varying forms, we redefine causal data from two novel
perspectives and then propose three data paradigms. Among them, the indefinite
data (like dialogues or video sources) induce low sample utilization and
incapability of the distribution assumption, both leading to the fact that
learning causal representation 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 the latent variable to construct evidence lower bound. By
this design ethos, The causal strengths of different structures are regarded as
a distribution and can be expressed as a 2D matrix. Moreover, considering the
latent confounders, we disentangle the causal graph G into two relation
subgraphs O and C. O contains pure relations between observed variables, while
C represents the relations from latent variables to observed variables. We
implement the above designs as a dynamic variational inference model, tailored
to learn causal representation from indefinite data under latent confounding.
Finally, we conduct comprehensive experiments on synthetic and real-world data
to demonstrate the effectiveness of our method.
