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

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 …

Hypergraphs provide an effective abstraction for modeling multi-way group interactions
among nodes, where each hyperedge can connect any number of nodes. Different from …

The convenient access to copious multifaceted data has encouraged machine learning
researchers to reconsider correlation-based learning and embrace the opportunity of …

From the perspective of historical review, the methodology of economics develops from
qualitative to quantitative, from a small sampling of data to a vast amount of data. Because of …

The convenient access to observational data enables us to learn causal effects without
randomized experiments. This research direction draws increasing attention in research …

Causal inference is the idea of cause-and-effect; this fundamental area of sciences can be
applied to problem space associated with Newton's laws or the devastating COVID-19 …

One fundamental problem in causal inference is to learn the individual treatment effects
(ITE)--assessing the causal effects of a certain treatment (eg, prescription of medicine) on an …

Estimating individual treatment effects from data of randomized experiments is a critical task
in causal inference. The Stable Unit Treatment Value Assumption (SUTVA) is usually made …

Recent years have witnessed a rocketing growth of machine learning methods on graph
data, especially those powered by effective neural networks. Despite their success in …