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

Causal inference is a powerful modeling tool for explanatory analysis, which might enable
current machine learning to become explainable. How to marry causal inference with …

Recently, machine/deep learning techniques are achieving remarkable success in a variety
of intelligent control and management systems, promising to change the future of artificial …

Learning to predict missing links is important for many graph-based applications. Existing
methods were designed to learn the association between observed graph structure and …

How to effectively mitigate the bias of feedback in recommender systems is an important
research topic. In this paper, we first describe the generation process of the biased and …

We consider the challenge of estimating treatment effects from observational data; and point
out that, in general, only some factors based on the observed covariates X contribute to …

For many machine learning algorithms, two main assumptions are required to guarantee
performance. One is that the test data are drawn from the same distribution as the training …

In many important machine learning applications, the training distribution used to learn a
probabilistic classifier differs from the distribution on which the classifier will be used to make …

In observational studies, confounder separation and balancing are the fundamental
problems of treatment effect estimation. Most of the previous methods focused on …

Causal inference has numerous real-world applications in many domains, such as health
care, marketing, political science, and online advertising. Treatment effect estimation, a …