Counterfactual Propagation for Semi-Supervised Individual Treatment Effect Estimation
This addresses the costly data collection issue in decision-making domains like healthcare or marketing, though it is incremental as it adapts existing techniques to a new setting.
The paper tackles the problem of estimating individual treatment effects (ITE) when labeled data is scarce by proposing a semi-supervised method that combines matching and label propagation, showing it can mitigate data scarcity in experiments with semi-real datasets.
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.