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

Instrumental variables (IVs), sources of treatment randomization that are conditionally
independent of the outcome, play an important role in causal inference with unobserved …

In causal inference, it is common to select a subset of observed covariates, named the
adjustment features, to be adjusted for estimating the treatment effect. For real-world …

Most existing graph neural networks (GNNs) are proposed without considering the selection
bias in data, ie, the inconsistent distribution between the training set with the test set. In …

The advent of the big data era brought new opportunities and challenges to draw treatment
effect in data fusion, that is, a mixed dataset collected from multiple sources (each source …

Purpose In the era of big data, there is doubt about the significance of causal inference as a
paramount scientific task in the social sciences. Meanwhile, data-mining techniques rooted …

One fundamental problem in causal inference is the treatment effect estimation in obser-
vational studies, and its key challenge is to handle the confounding bias induced by the …

One fundamental problem in causal inference is the treatment effect estimation in
observational studies, and its key challenge is to handle the confounding bias induced by …

Uplift modeling aims to measure the incremental effect, which we call uplift, of a strategy or
action on the users from randomized experiments or observational data. Most existing uplift …

The difficulty of causal inference for small-sample-size data lies in the issue of inefficiency
that the variance of the estimators may be large. Some existing weighting methods adopt the …