Causal Mamba for Estimating Counterfactual Outcomes over Time

Accurately and efficiently estimating counterfactual outcomes over time from observational data is essential in various applications. However, existing methods suffer from computational inefficiency and over-balancing, limiting their practical use. In this paper, we propose Causal Mamba (CM), a novel end-to-end framework for efficient counterfactual estimation over time. Its GCSI-Mamba backbone introduces Gated Cross-Sequence Interaction (GCSI) and follows an interact-then-propagate paradigm, first modeling dependencies among treatment, covariate, and outcome streams and then using stream specific Mamba branches to capture their respective temporal dynamics, thereby preserving stream heterogeneity while retaining linear complexity with respect to sequence length. The Multi-Task Collaboration (MTC) module further coordinates outcome prediction, treatment domain confusion, and covariate reconstruction to reduce treatment specific bias while preserving relevant information for counterfactual prediction, avoiding information loss caused by over-balancing. Experimental results on synthetic and real-world datasets demonstrate that CM not only outperforms baselines but also achieves greater runtime efficiency.