Automatic, Debiased, and Invariant Counterfactual Generation under General Interventions
ADIGen addresses a critical pain point in causal inference: generating reliable counterfactual predictions when interventions are complex and real-world data is messy. By combining Riesz regression, causal invariance, and doubly robust estimation, the framework tackles three endemic problems that have limited prior generative approaches: numerical instability, poor transfer across environments, and vulnerability to model misspecification. This matters because decision-support systems in healthcare, policy, and finance depend on counterfactual reasoning under distribution shift. The excess-risk bounds provide theoretical guarantees that practitioners can actually trust, moving causal generative modeling from research curiosity toward production viability.62
