Speaker: Dr. Kevin Bello

From: Soroco

Abstract

Interpretability and causality are key desiderata in modern machine learning systems. Graphical models, and more specifically directed acyclic graphs (DAGs, a.k.a. Bayesian networks), serve as a well-established tool for expressing interpretable causal relationships. However, the task of estimating DAG structures from data poses a significant challenge, given its inherently complex combinatorial nature, and traditional approaches rely on various local heuristics.

In this presentation, I will explore an innovative approach that fundamentally redefines the causal discovery problem as a smooth but nonconvex optimization problem that avoids combinatorial constraints entirely. Following an overview of this framework and recent advancements in understanding its properties, I will delve into recent progress in directly learning causal shifts and invariances from multiple datasets, bypassing the estimation of causal structures. Finally, I will provide an overview of open problems and my future research plans for causal machine learning.

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