The two fields of machine learning and graphical causality arose and are developed
separately. However, there is, now, cross-pollination and increasing interest in both fields to …

A concise and self-contained introduction to causal inference, increasingly important in data
science and machine learning. The mathematization of causality is a relatively recent …

The machine learning community's interest in causality has significantly increased in recent
years. My understanding of causality has been shaped by Judea Pearl and a number of …

The discovery of causal relationships from purely observational data is a fundamental
problem in science. The most elementary form of such a causal discovery problem is to …

We provide novel theoretical results regarding local optima of regularized M-estimators,
allowing for nonconvexity in both loss and penalty functions. Under restricted strong …

We study the class of location-scale or heteroscedastic noise models (LSNMs), in which the
effect $ Y $ can be written as a function of the cause $ X $ and a noise source $ N …

We investigate the problem of testing whether d possibly multivariate random variables,
which may or may not be continuous, are jointly (or mutually) independent. Our method …

We pose causal inference as the problem of learning to classify probability distributions. In
particular, we assume access to a collection {(S_i, l_i)} _i= 1^ n, where each S_i is a sample …

We establish finite-sample guarantees for a polynomial-time algorithm for learning a
nonlinear, nonparametric directed acyclic graphical (DAG) model from data. The analysis is …

The fusion of causal models with deep learning introducing increasingly intricate data sets,
such as the causal associations within images or between textual components, has surfaced …