Estimating Continuous Treatment Effects with Recourse Data

Recourse explanations describe what would need to change for a different outcome to occur, yet they are not typically used for treatment effect estimation. We study how such recourse data can be used for causal inference with continuous treatments. This provides counterfactual supervision beyond observed outcomes at realized treatment values, and is especially useful when routine treatment assignment leaves parts of the treatment domain unsupported, as in diverse applications such as drug dosing, healthcare operations, and recommendation systems. We formalize recourse explanations as structural boundary samples and show how they identify continuous dose-response curves under a positivity condition fundamentally distinct from standard overlap. Because recourses are observed only for negative-outcome units, the boundary distribution is left-truncated relative to the population; we correct this selection using Lynden–Bell inversion. We translate this result into an auxiliary recourse loss for neural dose-response estimators, converting recourse-derived boundaries into counterfactual supervision. Experiments on synthetic and semi-synthetic data show consistent improvements over standard baselines. More broadly, our work shows that recourse explanations can serve as structured causal evidence, expanding what can be learned from observational data.