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.

翻译：将深度学习与因果发现相结合，促使我们认识到在对话和视频中学习因果结构与表征充满挑战。我们将这些数据形式定义为“非确定数据”，其特点在于多结构数据与多值表征的共存。与现有可适配数据形式不同，非确定数据在数据集与方法层面仍存在缺口。为弥补数据集缺口，我们发布两个高质量数据集——Causalogue与Causaction，分别包含带有因果标注的文本对话样本与视频动作样本。此外，方法缺口源于多结构数据与多值表征的共存，这打破了所有现有方法的假设，使其无法适用于非确定数据。为此，我们提出一个概率框架作为基准模型，并针对该缺口设计三大亮点：1）利用非固定因果结构下噪声项的独立性建立表征的因果条件；2）将因果强度视为潜在变量，并在相关性空间中衡量重构损失；3）估计潜在混杂变量的影响。这些亮点使概率模型能够克服多结构数据与多值表征共存带来的挑战，并为潜在混杂变量的扩展奠定基础。综合实验评估了因果结构、因果表征及混杂解耦的基准结果。