Learning Balanced Causal Representations via Differentiable Propensity-Score Subclassification

Neural causal representation learning is increasingly used for average treatment effect (ATE) estimation. Many existing methods estimate propensity scores using binary cross-entropy loss, treating treatment-prediction accuracy as a proxy for adjustment quality. However, accurate propensity prediction from a learned representation does not in general ensure covariate balance or conditional independence between treatment assignment and covariates given the estimated score. We propose a plug-and-play subclassification-based balancing module that promotes covariate balance within propensity-score subclasses. The method incorporates subclassification into end-to-end neural training by jointly using a differentiable relaxation, dynamic programming to select subclass intervals, and iterative optimization. On the ACIC and IHDP benchmarks, our method reduces absolute ATE bias by approximately 0.035 and 0.062, respectively, compared with the strongest baseline. These results demonstrate that explicitly enforcing covariate balance can provide benefits beyond propensity-score prediction accuracy alone.