  Treatment effect estimation, which refers to the estimation of causal effects
and aims to measure the strength of the causal relationship, is of great
importance in many fields but is a challenging problem in practice. As present,
data-driven causal effect estimation faces two main challenges, i.e., selection
bias and the missing of counterfactual. To address these two issues, most of
the existing approaches tend to reduce the selection bias by learning a
balanced representation, and then to estimate the counterfactual through the
representation. However, they heavily rely on the finely hand-crafted metric
functions when learning balanced representations, which generally doesn't work
well for the situations where the original distribution is complicated. In this
paper, we propose a CETransformer model for casual effect estimation via
transformer based representation learning. To learn the representation of
covariates(features) robustly, a self-supervised transformer is proposed, by
which the correlation between covariates can be well exploited through
self-attention mechanism. In addition, an adversarial network is adopted to
balance the distribution of the treated and control groups in the
representation space. Experimental results on three real-world datasets
demonstrate the advantages of the proposed CETransformer, compared with the
state-of-the-art treatment effect estimation methods.

Machine Learning;Artificial Intelligence