Differentiable Causal Discovery For Latent Hierarchical Causal Models
This addresses a fundamental problem in causal discovery for researchers and practitioners dealing with complex, real-world data, offering a scalable and accurate method for nonlinear latent hierarchical models.
The paper tackles the challenge of discovering causal structures with latent variables from observational data by presenting new theoretical results on the identifiability of nonlinear latent hierarchical causal models and developing a novel differentiable causal discovery algorithm. It outperforms existing methods in accuracy and scalability, demonstrating utility on high-dimensional image data and downstream tasks.
Discovering causal structures with latent variables from observational data is a fundamental challenge in causal discovery. Existing methods often rely on constraint-based, iterative discrete searches, limiting their scalability to large numbers of variables. Moreover, these methods frequently assume linearity or invertibility, restricting their applicability to real-world scenarios. We present new theoretical results on the identifiability of nonlinear latent hierarchical causal models, relaxing previous assumptions in literature about the deterministic nature of latent variables and exogenous noise. Building on these insights, we develop a novel differentiable causal discovery algorithm that efficiently estimates the structure of such models. To the best of our knowledge, this is the first work to propose a differentiable causal discovery method for nonlinear latent hierarchical models. Our approach outperforms existing methods in both accuracy and scalability. We demonstrate its practical utility by learning interpretable hierarchical latent structures from high-dimensional image data and demonstrate its effectiveness on downstream tasks.