Optimal matching for heterogeneous treatment effect estimation

In observational studies, identifying subgroups and exploring heterogeneity is of practical significance. However, causal inference at the individual level is a challenging problem due to the absence of counterfactual outcomes and the presence of selection bias. To address this issue, we propose a general framework called TRIMATCH for estimating heterogeneous treatment effects. First, we find the optimal matching by solving a minimum average cost flow optimization problem in a tripartite graph network structure. Second, with the pseudo individual treatment effects acquired from the previous step, we establish a nonparametric regression model to predict heterogeneous treatment effects for individuals with diverse characteristics. Our experiments demonstrate the effectiveness of the proposed matching method and the interpretability of the results.