Nils Reimers and Iryna Gurevych. Sentence-bert: Sentence embeddings using siamese bert-networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 11 2019. URL http://arxiv.org/abs/1908.10084.
James M Robins and Andrea Rotnitzky. Semiparametric efficiency in multivariate regression models with missing data. Journal of the American Statistical Association, 90(429):122-129, 1995.
James M Robins, Andrea Rotnitzky, and Lue Ping Zhao. Estimation of Regression Coefficients When Some Regressors Are Not Always Observed. Journal of the American Statistical Association, 89 (427):846-866, 1994.
Andrea Rotnitzky and Stijn Vansteelandt. Double-robust methods. In Handbook of missing data methodology, pages 185-212. CRC Press, 2014.
Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu. Mpnet: Masked and permuted pre-training for language understanding. Advances in Neural Information Processing Systems, 33: 16857-16867, 2020.
Julie Tibshirani, Susan Athey, Erik Sverdrup, and Stefan Wager. grf: Generalized Random Forests, 2022. URL https://CRAN.R-project.org/package=grf. R package version 2.2.1.
Petter Tornberg. Chatgpt-4 outperforms experts and crowd workers in annotating political twitter messages with zero-shot learning. arXiv preprint arXiv:2304.06588, 2023.
Anastasios A Tsiatis. Semiparametric theory and missing data. Springer, 2006.
Aad W van der Vaart. Asymptotic Statistics, volume 3. Cambridge university press, 2000.
Stijn Vansteelandt and Oliver Dukes. Assumption-lean Inference for Generalised Linear Model Parameters. Journal of the Royal Statistical Society Series B: Statistical Methodology, 84(3): 657-685, 07 2022. ISSN 1369-7412. doi: 10.1111/rssb.12504. URL https://doi.org/10. 1111/rssb.12504.
Yamil Velez and Patrick Liu. Confronting core issues: A critical test of attitude polarization. APSA Preprints, 2023.
Siruo Wang, Tyler H McCormick, and Jeffrey T Leek. Methods for correcting inference based on outcomes predicted by machine learning. Proceedings of the National Academy of Sciences, 117 (48):30266-30275, 2020.
Patrick Y. Wu, Jonathan Nagler, Joshua A. Tucker, and Solomon Messing. Large language models can be used to estimate the latent positions of politicians. arXiv preprint arXiv:2303.12057, 2023.
Han Zhang. How using machine learning classification as a variable in regression leads to attenuation bias and what to do about it. SocArXiv, 2021.
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. Gender bias in coreference resolution: Evaluation and debiasing methods. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 15–20, New Orleans, Louisiana, June 2018. Association for Computational Linguistics. doi: 10.18653/v1/N18-2003. URL https://aclanthology.org/N18-2003.
Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. Calibrate before use: Improving few-shot performance of language models. In International Conference on Machine Learning, pages 12697-12706. PMLR, 2021.
Caleb Ziems, William Held, Omar Shaikh, Jiaao Chen, Zhehao Zhang, and Diyi Yang. Can large language models transform computational social science? arXiv preprint arXiv:2305.03514, 2023.