CE-RCFR: Robust Counterfactual Regression for Consensus-Enabled Treatment Effect Estimation

Estimating individual treatment effects (ITE) from observational data is challenging due to the absence of counterfactuals and the treatment selection bias. Prevalent ITE estimation methods tackle these challenges by aligning the treated and controlled distributions in the representational space. However, two critical issues have long been overlooked: (1)Mini-batch sampling sensitivity (MSS) issue, where representation distribution alignment at a mini-batch level is vulnerable to poor sampling cases, such as data imbalance and outliers; (2)Inconsistent representation learning (IRL) issue, where representation learning within a unified backbone network suffers from inconsistent gradient update directions due to the distribution skew between different treatment groups. To resolve these issues, we propose CE-RCFR, a <u>R</u>obust <u>C</u>ounter<u>F</u>actual <u>R</u>egression framework for <u>C</u>onsensus-<u>E</u>nabled causal effect estimation, including a relaxed distribution discrepancy regularizer (RDDR) module and a consensus-enabled aggregator (CEA) module. Specifically, for the robust representation alignment perspective, RDDR addresses the MSS issue by minimizing unbalanced optimal transport divergence between different treatment groups with a relaxed marginal constraint. For the accurate representation optimization perspective, CEA addresses the IRL issue by resolving the consistent gradient update directions on shared parameters within the backbone network. Extensive experiments demonstrate that CE-RCFR significantly outperforms the state-of-the-art methods in treatment effect estimations.