You can build causal models that are both powerful and interpretable by using Kolmogorov-Arnold Networks as the building blocks for structural equations—enabling you to see exactly how variables influence each other.
This paper introduces KaCGM, a causal generative model that uses Kolmogorov-Arnold Networks to learn causal relationships in tabular data. Unlike black-box approaches, each causal mechanism is interpretable and can be visualized or converted to symbolic equations, making it suitable for high-stakes applications like healthcare where understanding *why* a model makes decisions matters.