This work demonstrates improved causal estimation through optimal transport and covariate techniques, highlighting significant advancements in precision.
Key Points
The proposed method offers a direct, consistent estimator for causal bounds, improving reliability in inference.
Simulation results show that this approach outperforms traditional estimation techniques in reducing uncertainty.
By applying the adapted Wasserstein distance, we ensure the continuity of the optimal transport functional.
This study addresses challenges in partial identification by leveraging covariates for more effective causal inference.