Group-Adversarial Transfer for Causal Effect Estimation: Aligning Domains without Compromising Target-Domain Causal Information

Estimating average treatment effects (ATEs) is challenging under target-domain data scarcity. Although related source domains can provide additional information, distributional shifts may cause negative transfer. Effective transfer therefore requires reducing cross-domain discrepancies while preserving treatment-effect-relevant information and the treatment–control structure. We propose GADANet, a transfer causal learning framework that jointly learns representations for causal effect estimation and domain alignment. GADANet decomposes the learned representation into domain-alignment and treatment-effect subspaces and uses group-wise adversarial training to align source and target representations independently within treated and control groups. This design reduces cross-domain discrepancies while preserving treatment-effect-relevant information and avoiding cross-treatment alignment. We evaluate GADANet across eight simulation settings with varying target-domain sample sizes and distribution shift magnitudes. GADANet consistently achieves lower ATE errors, reducing error by up to $50.8\%$ relative to Baseline, $23.5\%$ relative to Warmstart, and $24.5\%$ relative to Pooling. Ablation studies confirm the contributions of both proposed components.