Interventional Causal Representation Learning

Understand

Causal representation learning seeks to extract high-level latent factors from low-level sensory data.

• Most existing methods rely on observational data and structural assumptions (e.g., conditional independence) to identify the latent factors.
• However, interventional data is prevalent across applications.
• Can interventional data facilitate causal representation learning? We explore this question in this paper.