  Individual treatment effect (ITE) represents the expected improvement in the
outcome of taking a particular action to a particular target, and plays
important roles in decision making in various domains. However, its estimation
problem is difficult because intervention studies to collect information
regarding the applied treatments (i.e., actions) and their outcomes are often
quite expensive in terms of time and monetary costs. In this study, we consider
a semi-supervised ITE estimation problem that exploits more easily-available
unlabeled instances to improve the performance of ITE estimation using small
labeled data. We combine two ideas from causal inference and semi-supervised
learning, namely, matching and label propagation, respectively, to propose
counterfactual propagation, which is the first semi-supervised ITE estimation
method. Experiments using semi-real datasets demonstrate that the proposed
method can successfully mitigate the data scarcity problem in ITE estimation.
