In healthcare, the development and deployment of insufficiently fair systems of artificial
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …

Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

Causal reasoning is a crucial part of science and human intelligence. In order to discover
causal relationships from data, we need structure discovery methods. We provide a review …

In the last few years, Artificial Intelligence (AI) has achieved a notable momentum that, if
harnessed appropriately, may deliver the best of expectations over many application sectors …

Causal inference is a critical research topic across many domains, such as statistics,
computer science, education, public policy, and economics, for decades. Nowadays …

Causal Machine Learning (CausalML) is an umbrella term for machine learning methods
that formalize the data-generation process as a structural causal model (SCM). This …

At the dawn of the fourth industrial revolution, we are witnessing a fast and widespread
adoption of artificial intelligence (AI) in our daily life, which contributes to accelerating the …

This work considers the question of how convenient access to copious data impacts our
ability to learn causal effects and relations. In what ways is learning causality in the era of …

Recommender systems have become crucial in information filtering nowadays. Existing
recommender systems extract user preferences based on the correlation in data, such as …

This paper addresses the use of neural networks for the estimation of treatment effects from
observational data. Generally, estimation proceeds in two stages. First, we fit models for the …