  With the widespread accumulation of observational data, researchers obtain a
new direction to learn counterfactual effects in many domains (e.g., health
care and computational advertising) without Randomized Controlled Trials(RCTs).
However, observational data suffer from inherent missing counterfactual
outcomes, and distribution discrepancy between treatment and control groups due
to behaviour preference. Motivated by recent advances of representation
learning in the field of domain adaptation, we propose a novel framework based
on Cycle-Balanced REpresentation learning for counterfactual inference (CBRE),
to solve above problems. Specifically, we realize a robust balanced
representation for different groups using adversarial training, and meanwhile
construct an information loop, such that preserve original data properties
cyclically, which reduces information loss when transforming data into latent
representation space.Experimental results on three real-world datasets
demonstrate that CBRE matches/outperforms the state-of-the-art methods, and it
has a great potential to be applied to counterfactual inference.
