Two important factors in modern machine learning systems are interpretability and causality. These factors help us determine the basis for decisions made by machine learning algorithms, which, in turn, improve the system’s performance and prevent unexpected failures. Understanding how these factors influence each other is crucial for building trustworthy and transparent models.

Graphical models, particularly directed acyclic graphs (DAGs), are effective tools for representing these relationships. They clearly illustrate how one factor can directly cause another. However, despite their intuitive nature, determining the structure of DAGs from data is challenging. This process often requires testing numerous possible combinations and relies heavily on heuristic methods.

In this talk, Kevin will present a fresh perspective on this challenge. Instead of traditional methods, he will demonstrate how to transform the problem into a smoother, more streamlined optimization process that avoids complex discrete combinations. This innovative approach opens up new possibilities for efficiently and generically discovering causal relationships in data.