Beyond DAGs: A Latent Partial Causal Model for Multimodal Learning

Understand

Directed Acyclic Graphs (DAGs) are a standard tool in causal modeling, but their suitability for capturing the complexity of large-scale multimodal data is questionable.

• In practice, real-world multimodal datasets are often collected from heterogeneous generative processes that do not conform to a single DAG.
• Instead, they may involve multiple, and even opposing, DAG structures with inverse causal directions.
• To address this gap, in this work, we first propose a novel latent partial causal model tailored for multimodal data representation learning, featuring two latent coupled variables parts connected by an undirected edge, to represent the transfer of knowledge across modalities.