  We regularly consider answering counterfactual questions in practice, such as
"Would people with diabetes take a turn for the better had they choose another
medication?". Observational studies are growing in significance in answering
such questions due to their widespread accumulation and comparatively easier
acquisition than Randomized Control Trials (RCTs). Recently, some works have
introduced representation learning and domain adaptation into counterfactual
inference. However, most current works focus on the setting of binary
treatments. None of them considers that different treatments' sample sizes are
imbalanced, especially data examples in some treatment groups are relatively
limited due to inherent user preference. In this paper, we design a new
algorithmic framework for counterfactual inference, which brings an idea from
Meta-learning for Estimating Individual Treatment Effects (MetaITE) to fill the
above research gaps, especially considering multiple imbalanced treatments.
Specifically, we regard data episodes among treatment groups in counterfactual
inference as meta-learning tasks. We train a meta-learner from a set of source
treatment groups with sufficient samples and update the model by gradient
descent with limited samples in target treatment. Moreover, we introduce two
complementary losses. One is the supervised loss on multiple source treatments.
The other loss which aligns latent distributions among various treatment groups
is proposed to reduce the discrepancy. We perform experiments on two real-world
datasets to evaluate inference accuracy and generalization ability.
Experimental results demonstrate that the model MetaITE matches/outperforms
state-of-the-art methods.
