Physics is a field of science that has traditionally used the scientific method to answer
questions about why natural phenomena occur and to make testable models that explain the …

We study the problem of learning causal representations from unknown, latent interventions
in a general setting, where the latent distribution is Gaussian but the mixing function is …

Causality has the potential to truly transform the way we solve a large number of real-world
problems. Yet, so far, its potential largely remains to be unlocked as causality often requires …

Learning graphical conditional independence structures is an important machine learning
problem and a cornerstone of causal discovery. However, the accuracy and execution time …

Recently, an intriguing class of non-convex optimization problems has emerged in the
context of learning directed acyclic graphs (DAGs). These problems involve minimizing a …

Recently, a new class of non-convex optimization problems motivated by the statistical
problem of learning an acyclic directed graphical model from data has attracted significant …

This paper investigates in which cases continuous optimization for directed acyclic graph
(DAG) structure learning can and cannot perform well and why this happens, and suggests …

We present a novel perspective and algorithm for learning directed acyclic graphs (DAGs)
from data generated by a linear structural equation model (SEM). First, we show that a linear …

We present a novel form of Fourier analysis, and associated signal processing concepts, for
signals (or data) indexed by edge-weighted directed acyclic graphs (DAGs). This means that …

Under stringent model type and variable distribution assumptions, differentiable score-
based causal discovery methods learn a directed acyclic graph (DAG) from observational …