  Learning causal effects from observational data greatly benefits a variety of
domains such as health care, education and sociology. For instance, one could
estimate the impact of a new drug on specific individuals to assist the clinic
plan and improve the survival rate. In this paper, we focus on studying the
problem of estimating Conditional Average Treatment Effect (CATE) from
observational data. The challenges for this problem are two-fold: on the one
hand, we have to derive a causal estimator to estimate the causal quantity from
observational data, where there exists confounding bias; on the other hand, we
have to deal with the identification of CATE when the distribution of
covariates in treatment and control groups are imbalanced. To overcome these
challenges, we propose a neural network framework called Adversarial
Balancing-based representation learning for Causal Effect Inference (ABCEI),
based on the recent advances in representation learning. To ensure the
identification of CATE, ABCEI uses adversarial learning to balance the
distributions of covariates in treatment and control groups in the latent
representation space, without any assumption on the form of the treatment
selection/assignment function. In addition, during the representation learning
and balancing process, highly predictive information from the original
covariate space might be lost. ABCEI can tackle this information loss problem
by preserving useful information for predicting causal effects under the
regularization of a mutual information estimator. The experimental results show
that ABCEI is robust against treatment selection bias, and matches/outperforms
the state-of-the-art approaches. Our experiments show promising results on
several datasets, representing different health care domains among others.
