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Adversarial Balancing-based Representation Learning for Causal Effect Inference with Observational Data
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 to improve the survive rate. In this paper,
we conduct causal inference with observational studies based on potential
outcome framework (PO) (Rubin, 2005). The central problem for causal effect
inference in PO is dealing with the unobserved counterfactuals and treatment
selection bias. The state-of-the-art approaches focus on solving these problems
by balancing the treatment and control groups (Sun and Nikolaev, 2016).
However, during the learning and balancing process, highly predictive
information from the original covariate space might be lost. In order to build
more robust estimators, we tackle this information loss problem by presenting a
method called Adversarial Balancing-based representation learning for Causal
Effect Inference (ABCEI), based on the recent advances in representation
learning. ABCEI uses adversarial learning to balance the distributions of
treatment and control group in the latent representation space, without any
assumption on the form of the treatment selection/assignment function. ABCEI
preserves 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