Bounded, efficient and multiply robust estimation of average treatment effects using instrumental variables

Citations

Cited by:

1. Byeong Yeob Choi, 2023. "Profiling compliers and noncompliers for instrumental variable analysis with covariates: A weighting approach," PLOS ONE, Public Library of Science, vol. 18(6), pages 1-25, June.
2. Linbo Wang & Eric Tchetgen Tchetgen & Torben Martinussen & Stijn Vansteelandt, 2023. "Rejoinder to discussions on “Instrumental variable estimation of the causal hazard ratio”," Biometrics, The International Biometric Society, vol. 79(2), pages 564-568, June.
3. Zhichao Jiang & Shu Yang & Peng Ding, 2022. "Multiply robust estimation of causal effects under principal ignorability," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1423-1445, September.
4. Benjamin R. Baer & Robert L. Strawderman & Ashkan Ertefaie, 2023. "Discussion on “Instrumental variable estimation of the causal hazard ratio,” by Linbo Wang, Eric Tchetgen Tchetgen, Torben Martinussen, and Stijn Vansteelandt," Biometrics, The International Biometric Society, vol. 79(2), pages 554-558, June.
5. Hongming Pu & Bo Zhang, 2021. "Estimating optimal treatment rules with an instrumental variable: A partial identification learning approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(2), pages 318-345, April.
6. Abhinandan Dalal & Patrick Blobaum & Shiva Kasiviswanathan & Aaditya Ramdas, 2024. "Anytime-Valid Inference for Double/Debiased Machine Learning of Causal Parameters," Papers 2408.09598, arXiv.org, revised Sep 2024.
7. Choi, Jin-young & Lee, Goeun & Lee, Myoung-jae, 2023. "Endogenous treatment effect for any response conditional on control propensity score," Statistics & Probability Letters, Elsevier, vol. 196(C).
8. Luo, Shanshan & Zhang, Yechi & Li, Wei & Geng, Zhi, 2025. "Multiply robust estimation of causal effects using linked data," Computational Statistics & Data Analysis, Elsevier, vol. 209(C).
9. Linbo Wang & Eric Tchetgen Tchetgen & Torben Martinussen & Stijn Vansteelandt, 2023. "Instrumental variable estimation of the causal hazard ratio," Biometrics, The International Biometric Society, vol. 79(2), pages 539-550, June.
10. Shi, Chengchun & Qi, Zhengling & Wang, Jianing & Zhou, Fan, 2023. "Value enhancement of reinforcement learning via efficient and robust trust region optimization," LSE Research Online Documents on Economics 122756, London School of Economics and Political Science, LSE Library.
11. Akanksha Negi & Didier Nibbering, 2025. "Identification of dynamic treatment effects when treatment histories are partially observed," Papers 2501.04853, arXiv.org, revised Jun 2025.
12. Jingwen Tang & Zhengling Qi & Ethan Fang & Cong Shi, 2025. "Offline Feature-Based Pricing Under Censored Demand: A Causal Inference Approach," Manufacturing & Service Operations Management, INFORMS, vol. 27(2), pages 535-553, March.
13. Mao, Lu, 2022. "Identification of the outcome distribution and sensitivity analysis under weak confounder–instrument interaction," Statistics & Probability Letters, Elsevier, vol. 189(C).
14. Byeong Yeob Choi, 2024. "Instrumental variable estimation of weighted local average treatment effects," Statistical Papers, Springer, vol. 65(2), pages 737-770, April.
15. Yuta Ota & Takahiro Hoshino & Taisuke Otsu, 2024. "Causal Inference With Auxiliary Observations," Keio-IES Discussion Paper Series 2024-022, Institute for Economics Studies, Keio University.
    • Yuta Ota & Takahiro Hoshino & Taisuke Otsu, 2025. "Causal Inference With Auxiliary Observations," Keio-IES Discussion Paper Series 2025-021, Institute for Economics Studies, Keio University.
16. Pan Zhao & Yifan Cui, 2023. "A Semiparametric Instrumented Difference-in-Differences Approach to Policy Learning," Papers 2310.09545, arXiv.org.
17. Myoung‐jae Lee, 2021. "Instrument residual estimator for any response variable with endogenous binary treatment," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(3), pages 612-635, July.
18. Shuyuan Chen & Peng Zhang & Yifan Cui, 2025. "Identification and Debiased Learning of Causal Effects with General Instrumental Variables," Papers 2510.20404, arXiv.org, revised Feb 2026.
19. Shuxiao Chen & Bo Zhang, 2021. "Estimating and Improving Dynamic Treatment Regimes With a Time-Varying Instrumental Variable," Papers 2104.07822, arXiv.org.
20. Dingke Tang & Dehan Kong & Wenliang Pan & Linbo Wang, 2023. "Ultra‐high dimensional variable selection for doubly robust causal inference," Biometrics, The International Biometric Society, vol. 79(2), pages 903-914, June.
21. Haoyu Wei & Hengrui Cai & Chengchun Shi & Rui Song, 2024. "On Efficient Inference of Causal Effects with Multiple Mediators," Papers 2401.05517, arXiv.org.
22. Yan Liu, 2022. "Policy Learning under Endogeneity Using Instrumental Variables," Papers 2206.09883, arXiv.org, revised Jan 2026.
23. Ting Ye & Ashkan Ertefaie & James Flory & Sean Hennessy & Dylan S. Small, 2023. "Instrumented difference‐in‐differences," Biometrics, The International Biometric Society, vol. 79(2), pages 569-581, June.
24. Martin Emil Jakobsen & Jonas Peters, 2020. "Distributional robustness of K-class estimators and the PULSE," Papers 2005.03353, arXiv.org, revised Mar 2022.
25. Cui, Yifan & Tchetgen Tchetgen, Eric, 2021. "On a necessary and sufficient identification condition of optimal treatment regimes with an instrumental variable," Statistics & Probability Letters, Elsevier, vol. 178(C).
26. Shaojie Wei & Chao Zhang & Zhi Geng & Shanshan Luo, 2024. "Identifiability and Estimation for Potential-Outcome Means with Misclassified Outcomes," Mathematics, MDPI, vol. 12(18), pages 1-19, September.
27. Shixiao Zhang & Peisong Han & Changbao Wu, 2023. "Calibration Techniques Encompassing Survey Sampling, Missing Data Analysis and Causal Inference," International Statistical Review, International Statistical Institute, vol. 91(2), pages 165-192, August.