Gradient-based causal discovery

In this section, we’ll introduce gradient-based causal discovery methods. We’ll discuss the main contributions of this family of methods and their main disadvantages. Finally, we’ll implement selected methods using gCastle and compare their performance with other families.

What exactly is so gradient about you?

2018 was an exciting year for the causal discovery community. Xun Zheng from CMU and his colleagues presented an interesting paper during the 2018 NeurIPS conference.

The work was titled DAGs with NO TEARS: Continuous Optimization for Structure Learning and introduced a novel approach to causal structure learning (though we need to say that the authors did not explicitly state that their method is causal).

The proposed method (called NOTEARS) was not based on a set of independence tests or local heuristics but rather treated the task of structure learning as a joint, continuously-optimized task.

One of the main...