Nonlinear Causal Discovery through a Sequential Edge Orientation Approach

The exploration of causal discovery through directed acyclic graphs (DAGs) is gaining traction, as highlighted in the recent study on nonlinear causal discovery. This research addresses the limitations of existing methods that often rely on restrictive assumptions and extensive computational time. The proposed sequential edge orientation approach, leveraging the pairwise additive noise model (PANM), aligns with advancements in generative models, such as those seen in diffusion models for image restoration. These models have shown significant improvements in performance, suggesting a broader trend in AI towards enhancing computational efficiency and accuracy across various applications, including causal inference and image processing.