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A New Representation Learning Method for Individual Treatment Effect Estimation: Split Covariate Representation Network

Individual treatment effect (ITE) estimation is widely used in many essential fields, such as medical and education. But two problems, unknown counterfactual outcome and confounder, are the barriers for making a good ITE estimation. Although some representation learning methods based on potential outcome framework have been proposed to solve the problems, we find that most of previous works assume all features (also named covariate) of a unit are confounders. However, this assumption is not easy to become true, because instrument variables, adjustment variables and irrelevant variables can also be included in features. Therefore, this paper proposes a simple method to split covariates, and then a network, Split Covariate Representation Network (SCRNet), is mentioned, which is used to estimate ITE by different kinds of variables. Experiment results show that our method outperforms other state-of-arts methods on IHDP, a semi-synthetic dataset, and Jobs, a real-world dataset.