This paper aims to give a broad coverage of central concepts and principles involved in
automated causal inference and emerging approaches to causal discovery from iid data and …

Methods of transfer learning try to combine knowledge from several related tasks (or
domains) to improve performance on a test task. Inspired by causal methodology, we relax …

Two apparently unrelated fields—normalizing flows and causality—have recently received
considerable attention in the machine learning community. In this work, we highlight an …

Label noise generally degenerates the performance of deep learning algorithms because
deep neural networks easily overfit label errors. Let $ X $ and $ Y $ denote the instance and …

Statistical learning relies upon data sampled from a distribution, and we usually do not care
what actually generated it in the first place. From the point of view of causal modeling, the …

In many scientific fields, such as economics and neuroscience, we are often faced with
nonstationary time series, and concerned with both finding causal relations and forecasting …

We study the problem of causal structure learning in linear systems from observational data
given in multiple domains, across which the causal coefficients and/or the distribution of the …

Today's methods for uncovering causal relationships from observational data either
constrain functional assignments (linearity/additive noise assumptions) or the data …

In real life, accurately annotating large-scale datasets is sometimes difficult. Datasets used
for training deep learning models are likely to contain label noise. To make use of the …

To determine causal relationships between two variables, approaches based on Functional
Causal Models (FCMs) have been proposed by properly restricting model classes; however …