| Causal inference methods for combining randomized trials and observational studies: a review B Colnet, I Mayer, G Chen, A Dieng, R Li, G Varoquaux, JP Vert, J Josse, ... Statistical science 39 (1), 165-191, 2024 | 259 | 2024 |
| Propensity score matching and subclassification in observational studies with multi‐level treatments S Yang, GW Imbens, Z Cui, DE Faries, Z Kadziola Biometrics 72 (4), 1055-1065, 2016 | 185 | 2016 |
| A review of spatial causal inference methods for environmental and epidemiological applications BJ Reich, S Yang, Y Guan, AB Giffin, MJ Miller, A Rappold International Statistical Review 89 (3), 605-634, 2021 | 151 | 2021 |
| Combining multiple observational data sources to estimate causal effects S Yang, P Ding Journal of the American Statistical Association, 2020 | 138 | 2020 |
| Statistical data integration in survey sampling: A review S Yang, JK Kim Japanese Journal of Statistics and Data Science 3 (2), 625-650, 2020 | 130 | 2020 |
| Asymptotic inference of causal effects with observational studies trimmed by the estimated propensity scores S Yang, P Ding Biometrika 105 (2), 487-493, 2018 | 124 | 2018 |
| Doubly robust inference when combining probability and non-probability samples with high dimensional data S Yang, JK Kim, R Song Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2020 | 113 | 2020 |
| Elastic integrative analysis of randomised trial and real-world data for treatment heterogeneity estimation S Yang, C Gao, D Zeng, X Wang Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2023 | 91 | 2023 |
| Improving trial generalizability using observational studies D Lee, S Yang, L Dong, X Wang, D Zeng, J Cai Biometrics 79 (2), 1213-1225, 2023 | 91 | 2023 |
| Causal inference with confounders missing not at random S Yang, L Wang, P Ding Biometrika 106 (4), 875-888, 2019 | 88 | 2019 |
| Integrative -learner of heterogeneous treatment effects combining experimental and observational studies L Wu, S Yang Conference on Causal Learning and Reasoning, 904-926, 2022 | 57 | 2022 |
| Propensity score weighting for causal inference with clustered data S Yang Journal of Causal Inference 6 (2), 20170027, 2018 | 56 | 2018 |
| Multiply robust estimation of causal effects under principal ignorability Z Jiang, S Yang, P Ding Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2022 | 55 | 2022 |
| Fractional imputation in survey sampling: A comparative review S Yang, JK Kim | 55 | 2016 |
| Risks associated with discontinuation of oral anticoagulation in newly diagnosed patients with atrial fibrillation: results from the GARFIELD‐AF Registry F Cools, D Johnson, AJ Camm, JP Bassand, FWA Verheugt, S Yang, ... Journal of Thrombosis and Haemostasis 19 (9), 2322-2334, 2021 | 51 | 2021 |
| Transfer learning of individualized treatment rules from experimental to real-world data L Wu, S Yang Journal of Computational and Graphical Statistics 32 (3), 1036-1045, 2023 | 48 | 2023 |
| Improved inference for heterogeneous treatment effects using real-world data subject to hidden confounding S Yang, D Zeng, X Wang arXiv preprint arXiv:2007.12922, 2020 | 46 | 2020 |
| Generalized propensity score approach to causal inference with spatial interference A Giffin, BJ Reich, S Yang, AG Rappold Biometrics 79 (3), 2220-2231, 2023 | 45 | 2023 |
| Sensitivity analysis for unmeasured confounding in coarse structural nested mean models S Yang, J Lok Statistica Sinica, doi:10.5705/ss.202016.0133, 2017 | 45 | 2017 |
| Semiparametric estimation of structural failure time models in continuous-time processes S Yang, K Pieper, F Cools Biometrika 107 (1), 123-136, 2020 | 43 | 2020 |
Jae kwang KimLAS Dean's professor, Statistics Department, Iowa State UniversityVerified email at iastate.edu
