1. Athey S., Chetty R., Imbens G. W., Kang H. The Surrogate Index: Combining Short-Term Proxies to Estimate Long-Term Treatment Effects more Rapidly and Precisely. Review of Economic Studies, 2025. DOI:https://doi.org/10.1093/restud/rdaf087.

2. Bibaut A., Kallus N., Ejdemyr S., Zhao M. Long-term causal inference with imperfect surrogates using many weak experiments, proxies, and cross-fold moments. arXiv preprint arXiv:2311.04657, 2023.

3. Cai R., Chen W., Yang Z., et al. Long-term causal effects estimation via latent surrogates representation learning. arXiv preprint arXiv:2208.04589, 2023.

4. de Luna X., Fowler P., Johansson P. Proxy variables and nonparametric identification of causal effects. Economics Letters, 2017, vol. 150, pp. 152–154. DOI:https://doi.org/10.1016/j.econlet.2016.11.018.

5. Kuang K., Li L., Geng Z., et al. Causal inference. Engineering, 2020, vol. 6, pp. 253–263. DOI:https://doi.org/10.1016/j.eng.2019.08.016.

6. VanderWeele T. J. Surrogate measures and consistent surrogates. Biometrics, 2013, vol. 69, no. 3, pp. 561–569. DOI:https://doi.org/10.1111/biom.12071.

7. Witherell P., Mahadevan S., Nath P., Senthilnathan A. Surrogate modeling of microstructure prediction in additive manufacturing. Computational Materials Science, 2024, vol. 247, art. 113536. DOI:https://doi.org/10.1016/j.commatsci.2024.113536.

8. Zhang V., Zhao M., Le A., et al. Evaluating the surrogate index as a decision-making tool using 200 A/B tests at Netflix. arXiv preprint arXiv:2311.11922, 2024.

9. Zeng Z., Arbour D., Feller A., Addanki R., Rossi R., Sinha R., Kennedy E. H. Continuous Treatment Effects with Surrogate Outcomes. arXiv preprint arXiv:2402.00168, 2024, 30 p. DOI:https://doi.org/10.48550/arXiv.2402.00168.

10. Hung T.-C., Chen Y.-C. The Proximal Surrogate Index: Long-Term Treatment Effects under Unobserved Confounding. arXiv preprint arXiv:2601.17712, 2026. DOI:https://doi.org/10.48550/arXiv.2601.17712.