  Integrating deep learning and causal discovery has encouraged us to spot that
learning causal structures and representations in dialogue and video is full of
challenges. We defined These data forms as "Indefinite Data", characterized by
multi-structure data and multi-value representations. Unlike existing adaptable
data forms, Indefinite Data still faces gaps in datasets and methods. To
address the dataset gap, we release two high-quality datasets - Causalogue and
Causaction, containing text dialogue samples and video action samples with
causal annotations respectively. Moreover, the method gap arises from the
coexistence of multi-structure data and multi-value representations, breaking
the assumptions of all current methods and rendering them infeasible on
Indefinite Data. To this end, we propose a probabilistic framework as a
baseline, incorporating three designed highlights for this gap: 1) establishing
Causation Condition of representations using the independence of noise terms
under non-fixed causal structures, 2) treating causal strength as a latent
variable and measuring the reconstruction loss in the correlation space, and 3)
estimating the effects of latent confounders. These highpoints make the
probabilistic model capable of overcoming challenges brought by the coexistence
of multi-structure data and multi-value representations and pave the way for
the extension of latent confounders. Comprehensive experiments have evaluated
baseline results of causal structures, causal representations, and confounding
disentanglement.
