Causal Structure-Guided Optimal Transport for Treatment Effect Estimation

Treatment effect estimation seeks to evaluate the effect of a treatment based on non-randomized trial data. To reduce the confounding bias between the control and treated groups, existing methods usually align the distributions of the groups by learning balanced representations from covariates. Despite their success, these methods simply model the covariates as a flat vector while ignoring the intrinsic causal structure among variables. To address this limitation, we exploit the causal structure among covariates to learn balanced representations for causal effect estimation. Specifically, we incorporate the causal structure into the framework of optimal transport, which has demonstrated a powerful ability for distribution alignment. We first discover the causal structure and establish the structural equations of covariates, and introduce the residuals into optimal transport, where the residuals capture individual information for heterogeneous treatment effect estimation. We further leverage the causal structure to refine the cost function in optimal transport, deriving a causal structure-aware transport plan. Theoretically, we reveal that the estimation error of the effect can be bounded by the proposed causal structure-guided optimal transport cost. Empirically, we conduct experiments on synthetic and real-world datasets to demonstrate the effectiveness of our proposed method.