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Individual treatment effect (ITE) represents the expected improvement in theoutcome of taking a particular action to a particular target, and playsimportant roles in decision making in various domains. However, its estimationproblem is difficult because intervention studies to collect informationregarding the applied treatments (i.e., actions) and their outcomes are oftenquite expensive in terms of time and monetary costs. In this study, we considera semi-supervised ITE estimation problem that exploits more easily-availableunlabeled instances to improve the performance of ITE estimation using smalllabeled data. We combine two ideas from causal inference and semi-supervisedlearning, namely, matching and label propagation, respectively, to proposecounterfactual propagation, which is the first semi-supervised ITE estimationmethod. Experiments using semi-real datasets demonstrate that the proposedmethod can successfully mitigate the data scarcity problem in ITE estimation.

Counterfactual Propagation for Semi-Supervised Individual Treatment Effect Estimation