Publications

 

Topics

  1. Sensitivity analysis [SensA]
  2. Principal stratification [PrinS]
  3. Spatial causal inference [SpaCI]
  4. Matching estimators [Matching]
  5. Estimand & control-based imputation: [CBI]
  6. Irregular and informative observation times: [IIOT]
  7. Longitudinal causal inference: marginal structural models and structural nested models [MSM&SNM]
  8. Data integration: review [DI:review]; generalize/transport RCT findings [genRCT]; test then pool [DI:ttp]; bias function modeling and correction [DI:bf]; selective borrowing [DI:sb]

AI/ML Conference proceedings

* Student or intern collaborator; + corresponding author

  1. Y. Zhang, F. Xie, S. Yang, and C. Wu (2026). Meta-router: bridging gold-standard and preference-based evaluations in LLM routing. ICLR (The 14th International Conference on Learning Representations) [arxiv]
  2. Y. Liu*, A. W. Levis, K. Zhu, S. Yang, P. Gilbert, and L. Han (2026). Targeted data fusion for causal survival analysis under distribution shift. ICLR (The 14th International Conference on Learning Representations) [arxiv]

    3. ** Winner of the 2025 IMS Hannan Graduate Student Travel Award, DISS Best Poster Award, Workshop on B&B Best Student Poster Award, and NESS Best Presentation Award

  3. [PrinS] S. Park, W. Lu, and S. Yang (2025). Evaluating and Learning Optimal Dynamic Treatment Regimes under Truncation by Death, NeurIPS (The 39th Annual Conference on Neural Information Processing Systems) [arxiv] [R: PFCI]
  4. [DI:sb] K. Zhu*, S. Yang+, and X. Wang (2025). Enhancing statistical validity and power in hybrid controlled trials: a randomization inference approach with conformal selective borrowing. ICML (The 41st International Conference on Machine Learning). [arxiv] [code: intFRT]

    5. ** Winner of the 2025 NISS New Researchers Presentation Award

  5. K. Zhu*, J. Chu*, W. Ye, I. Lipkovich, and S. Yang+ (2025). Doubly Robust Fusion of High-Dimensional Treatments for Policy Learning. ICML (The 41st International Conference on Machine Learning). [arxiv]
  6. [DI:sb] C. Gao*, S. Yang+, M. Shan, W. Ye, and I. Lipkovich (2025). Doubly protected estimation for survival outcomes utilizing external controls for randomized clinical trials. ICML (The 41st International Conference on Machine Learning). [arxiv]

    7. ** Winner of the 2025 Student Paper Competition Award at the LiDA conference

  7. J. Chu*, S. Yang, W. Lu, and P. Ghosh (2025). Efficient causal decision making with one-sided feedback. ICLR (The 30th International Conference on Learning Representations).
  8. J. Chu*, S. Yang , W. Lu, and P. Ghosh (2024). A shadow variable approach to policy evaluation and learning with one-sided feedback. NeurIPS (The 38th Annual Conference on Neural Information Processing Systems).

    ** Selected as an oral presentation at the NeurIPS 2024 CRL NeurIPS workshop

  9. Y. Cheng* and S. Yang (2024). Inference for Optimal Linear Treatment Regimes in Personalized Decision-making. UAI (The 40th Conference on Uncertainty in Artificial Intelligence) [arxiv]

    ** Selected as an oral presentation

  10. P. Zhao, J. Josse, A. Chambaz, and S. Yang (2024). Positivity-free Policy Learning with Observational Data. AISTAT (The 27th International Conference on Artificial Intelligence and Statistics), 238:1918-192. [arxiv] [code]

    ** Top 1% selected as an oral presentation

  11. [PrinS] J. Chu*, S. Yang, and W. Lu (2023). Multiply robust off-policy evaluation and learning under truncation by death, ICML (The 40th International Conference on Machine Learning), 202, 6195–6227.
  12. [DI:bf] Y. Cheng*, L. Wu, and S. Yang (2023). Enhancing treatment effect estimation: a model robust approach integrating randomized experiments and external controls using the double penalty integration estimator. UAI (The 39th Conference on Uncertainty in Artificial Intelligence) 2023. [arxiv]
  13. [DI:bf] L. Wu* and S. Yang+ (2022). Integrative R-learner of heterogeneous treatment effects combining experimental and observational studies. CLEAR (The 1st Conference on Causal Learning and Reasoning), 140, 1–S5.

Journal articles

* Student or intern collaborator; + corresponding author

  1. S. Pal, D. Ghosh, and S. Yang (2026). Penalized FCI for causal structural learning in a sparse DAG for biomarker discovery in Parkinson’s disease. The Annals of Applied Statistics [arxiv]
  2. D. Son, B. J. Reich, E. M. Schliep, S. Yang, and D. A. Gill (2026). Spatial causal inference in the presence of preferential sampling to study the impacts of marine protected areas. The Annals of Applied Statistics [arxiv]

    3. ** Winner of the 2025 ENAR Distinguished Student Paper Competition Award

  3. X. Zhou*, S. Yang, and B. Reich (2026). Estimating the causal effect of redlining on present-day air pollution. Biometrics, 82, ujaf173. [arxiv]
  4. G. Torrealba-Acosta, S. Yang, J. Calvo-Marin, and M. Lutz (2026). Identifying a proteomics signature of cognitive impairment and dementia in blood and CSF through a mediation analysis framework. Neurobiology of Aging, 157, 79-88.
  5. Y. Liu, K. Zhu, L. Han, and S. Yang+ (2025). COADVISE: Covariate adjustment with variable selection in randomized controlled trials. Journal of the Royal Statistical Society: Series A, doi: 10.1093/jrsssa/qnaf171 [arxiv] [R: coadvise]

    6. ** Winner of the 2025 NERDS (New England Rare Disease Statistics) Best Poster Award

  6. [genRCT] A. Wang, K. Wentzlof, J. Rajala, M. Green, Y. Zhang, and S. Yang (2025). Transfer learning for individualized treatment rules with application to sepsis patients data, Journal of Data Science [arxiv]
  7. [IIOT] T. Hong*, W. Lu, S. Yang, and P. Ghosh (2025). Multivariate zero-inflated causal model for regional mobility restriction effects on consumer spending, Journal of Causal Inference [arxiv]
  8. Y. Wang*, Y. Liu*, and S. Yang (2025). Rate doubly robust estimation for weighted average treatment effects. Journal of Causal Inference [arxiv] [R code WATE]
  9. [DI:bf] G. Mao, S. Yang+, and X. Wang (2025). Statistical inference for heterogeneous treatment effect with right-censored data from synthesizing randomized clinical trials and real-world data. Biometrics [arxiv]
  10. [SensA] Y. Fang, P. Mishra-Kalyani, X. Zhang, S. Gruber, S. Yang, P. Ding, M. Shan, J.Y. Lee, M. van der Laan, D. Faries, and H. Lee (2025). Sensitivity analysis for unmeasured confounding in medical product development and evaluation using real world evidence. Statistics in Biopharmaceutical Research, doi.org/10.1080/19466315.2025.2546360 [arxiv]
  11. [DI:bf] S. Yang+, S. Liu, D. Zeng, and X. Wang (2025). Data fusion methods for the heterogeneity of treatment effect and confounding function. Bernoulli, 31, 2987-3012. [arxiv]
  12. [DI:sb] C. Gao*, S. Yang+, M. Shan, W. Ye, I. Lipkovich, and D. Faries (2025). Improving randomized controlled trial analysis with data-adaptive borrowing. Biometrika, doi:10.1093/biomet/asae069. [arxiv] [code]

    13. ** Winner of the 2024 ICSA Student Paper Award

  13. [PrinS] Y. Zhang and S. Yang+. Semiparametric Localized Principal Stratification Analysis with Continuous Strata. Journal of the Royal Statistical Society: Series B, doi:10.1093/jrsssb/qkaf034 [arxiv]
  14. [Matching] [SpaCI] Y. Zhang*, C. Cui*, S. Yang, B. J. Reich, and D. Gill (2025). Matching Estimators of Causal Effects in Clustered Observational Studies. Journal of Causal Inference [arxiv]
  15. [genRCT] K. Jiang, X. Lai, S. Yang, Y. Gao and X. Zhou (2025). A practical analysis procedure on generalizing comparative effectiveness in randomized clinical trial to the real-world trial-eligible population. Journal of the Biopharmaceutical Statistics, doi:10.1080/10543406.2025.2489282. [arxiv]
  16. S. Yang and J.K. Kim (2025). Discussion on “Causal and counterfactual views of missing data models”. Statistica Sinica, doi:10.5705/ss.202025.0165.
  17. [DI:bf] X. Ye, S. Yang+, X. Wang, and Y. Liu (2025). Integrative analysis of high-dimensional RCT and RWD subject to censoring and hidden confounding. Lifetime Data Science, doi:10.1007/s10985-025-09654-1 [arxiv]
  18. [SensA] C. Gao*, X. Zhang, and S. Yang+ (2025). Doubly robust omnibus sensitivity analysis of externally controlled trials with intercurrent events, Biometrics, doi: 10.1093/biomtc/ujaf047 [arxiv] [code]
  19. X. Tan*, S. Yang+, W. Ye, D. E. Faries, I. Lipkovich, Z. Kadziola (2025). Double Machine Learning Methods for Estimating Average Treatment Effects: A Comparative Study. Journal of Biopharmaceutical Statistics, doi:10.1080/10543406.2025.2489281 [arxiv]
  20. P. Zhao, J. Josse, and S. Yang (2025). Efficient and robust transfer learning of optimal individualized treatment regimes with right-censored survival data. Journal of Machine Learning Research [arxiv] [code]
  21. S. Yang+ and P. Ding (2025). Two-phase rejective sampling and its asymptotic properties. Journal of the Royal Statistical Society: Series B, doi: 10.1093/jrsssb/qkaf002. [arxiv]
  22. E. Cho* and S. Yang (2025). Variable selection for doubly robust causal inference. Statistics and Its Interface, 18, 93-105. [arxiv]
  23. [genRCT] S. Yang+ and X. Zhang (2025). [Letter to Editor] Response to comment on “Transporting survival of an HIV clinical trial to the external target populations by Lee et al. (2024)”. Journal of Biopharmaceutical Statistics, 35, 467-471.
  24. [SensA] D. Faries, C. Gao, X. Zhang, C. Hazlett, J. Stamey, S. Yang, et al (2024). Real effect or bias? Best practices for evaluating the robustness of real-world evidence through quantitative sensitivity analysis for unmeasured confounding, Pharmaceutical Statistics, doi:10.1002/pst.2457. [Authorea] [arxiv]
  25. [IIOT] P. Sang, D. Kong, and S. Yang+ (2024). Functional principal component analysis for longitudinal observations with sampling at random. Biometrika, doi:10.1093/biomet/asae055. [arxiv]
  26. [IIOT] B. Smith, Y. Gao, S. Yang, A. Apter, and D. Scharfstein (2024). Semiparametric sensitivity analysis for trials with irregular and informative assessment times. Biometrics [arxiv]
  27. [Matching] H. Zhao*, T. Wang, S. Yang, Z. Cui, I. Lipkovich, and D. Fairs (2024). Propensity score matching for estimation of pairwise marginal hazard ratios. Communications in Statistics – Theory and Methods, doi.org/10.1080/03610926.2024.2419897.
  28. [SpaCI] L. Wu*, C. Gao*, S. Yang+, B. J. Reich, and A. Rappold (2024). Estimating spatially varying health effects in app-based citizen science research. Journal of the Royal Statistical Society: Series C, doi.org/10.1093/jrsssc/qlae034 [arxiv]

    ** Winner of the 2021 ASA Section on Statistics in Epidemiology Young Investigator Award

    ** Winner of the IMB Student Research Award from the 34th New England Statistics Symposium

  29. [IIOT] [MSM&SNM] J. Coulombe and S. Yang (2024). Multiply robust estimation of marginal structural models in observational studies subject to covariate-driven observations. Biometrics, 80, ujae065. [arxiv]
  30. C. Gao*, Z. Zhang, and S. Yang+ (2024). Causal Customer Churn Analysis with Low-rank Tensor Block Hazard Model. ICML [arxiv] [code]
  31. C. Gao*, S. Yang, and A. Zhang. Enhancing convolution neural network generalizability via low-rank weight approximation. IET Image Processing, 10.1049/ipr2.13205. [arxiv]
  32. [Matching] T. Wang*, H. Zhao*, S. Yang+, S. Tang, Z. Cui. L. Li, D. Faries (2024). Propensity score matching for estimating a marginal hazard ratio. Statistics in Medicine, doi:10.1002/sim.10103. [arxiv] [code]

    ** Winner of the 2021 ENAR Distinguished Student Paper Competition Award

  33. [CBI] S. Fairfax* and S. Yang (2024). Distributional imputation for the analysis of censored recurrent events. Statistics in Medicine, 43, 2622–2640. [R: code]

    ** Winner of the 2023 JSM Poster Award Competition Honorable Mention

  34. [DI:review] B. Colnet, I. Mayer, G. Chen, A. Dieng, R. Li, G. Varoquaux, J.P. Vert, J. Josse+, S. Yang+ (2024). Causal inference methods for combining randomized trials and observational studies: a review. Statistical Science, 1, 165–191. [arxiv]
  35. [SpaCI] D. Gill, S. Lester, C. Free, A. Pfaff, E. Lversen, B. Reich, S. Yang, et al (2024). A diverse portfolio of marine protected areas can better advance global conservation and equity. Proceedings of National Academy of Sciences, 10.1073/pnas.2313205121.
  36. [genRCT] D. Lee*, C. Gao*, S. Ghosh, and S. Yang+ (2024). Transporting survival of an HIV clinical trial to the external target populations. Journal of Biopharmaceutical Statistics, doi.org/10.1080/10543406.2024.2330216. [arxiv] [code]
  37. [genRCT] D. Lee*, S. Yang, M. Berry, T. Stinchcombe, H. Cohen, and X. Wang (2024). genRCT: A Statistical Analysis Framework for Generalizing RCT Findings to Real-World Population. Journal of Biopharmaceutical Statistics, doi.org/10.1080/10543406.2024.2333136. [code]
  38. X. Mao, H. Wang, Z. Wang, and S. Yang (2024). Mixed dataframe matrix completion in survey under heterogeneous missingness. Journal of Computation and Graphical Statistics, doi.org/10.1080/10618600.2024.2319154. [arxiv]
  39. [CBI] S. Liu*, S. Yang+, Y. Zhang, and G. Liu (2024). Multiply robust estimators in longitudinal studies with missing data under control-based imputation. Biometrics, 80, ujad036. [arxiv] [R: mrJ2R]

    40. ** Winner of the 2023 ASA BIOP RISW Student Travel Award

    ** Winner of the 2024 ENAR RAB Student Poster Award Competition

  40. Q. Guan* and S. Yang+ (2024). A unified framework for causal inference with multiple imputation using martingales. Statistica Sinica, 34, 1649-1673. [arxiv]
  41. [DI:ttp] S. Yang, C. Gao*, X. Wang, and D. Zeng (2023). Elastic integrative analysis of randomized trial and real-world data for treatment heterogeneity estimation. Journal of the Royal Statistical Society: Series B, 85, 575-596. [arxiv]
  42. C. Gao*, S. Yang+, and J. K. Kim (2023). Soft calibration for correcting selection bias under mixed-effects models. Biometrika, 110, 897–911. [arxiv]
  43. [DI:ttp] C. Gao* and S. Yang (2023). Pretest estimation in combining probability and non-probability samples. Electronic Journal of Statistics, 17, 1492–1546. [arxiv]
  44. J. Chu*, W. Lu, and S. Yang+ (2023). Targeted optimal treatment regime learning using summary statistics. Biometrika, 110, 913–931. [arxiv]
  45. [SpaCI] Y. Guan, G. L. Page, B. J. Reich, M. Ventrucci, and S. Yang (2023). A spectral adjustment for spatial confounding. Biometrika, 110, 699–719. [arxiv]
  46. [MSM&SNM] M. Yu*, W. Lu, S. Yang, and P. Ghosh (2023). Multiplicative structural nested mean model for zero-inflated outcomes. Biometrika, 110, 519–536.
  47. [CBI] S. Yang, Y. Zhang, G. Liu, and Q. Guan (2023). SMIM: a unified framework of Survival sensitivity analysis using Multiple Imputation and Martingale. Biometrics, 79, 230–240. [arxiv] [R: smim]
  48. [CBI] S. Liu*, Y. Zhang, G. T. Golm, G. Liu, and S. Yang+ (2023). Robust analyses for longitudinal clinical trials with dropouts and non-normal continuous outcomes. Statistical Theory and Related Fields, 8, 1-14. [arxiv]
  49. [CBI] S. Liu*, S. Yang+, Y. Zhang, and G. Liu (2023). Sensitivity analysis in longitudinal clinical trials via distributional imputation. Statistical Methods in Medical Research, 32, 181–194. [link] [arxiv] [R: code]
  50. [Matching] S. Yang and Y. Zhang (2023). Multiply robust matching estimators of average and quantile treatment effects. Scandinavian Journal of Statistics, 50, 235–265. [arxiv]
  51. [genRCT] L. Wu*, and S. Yang (2023). Transfer learning of individualized treatment rules from experimental to real-world data. Journal of Computation and Graphical Statistics, 32, 1036–1045. [arxiv]
  52. D. Kong, S. Yang, and L. Wang (2022). Identifiability of causal effects with multiple causes and a binary outcome. Biometrika, 109, 265–272. doi:10.1093/biomet/asab016. [arxiv]
  53. [PrinS] Z. Jiang, S. Yang, and P. Ding (2022). Multiply robust estimation of causal effects under principal ignorability. Journal of the Royal Statistical Society: Series B, 84, 1423–1445. [arxiv] [code]
  54. [genRCT] D. Lee*, S. Yang+, L. Dong, X. Wang, D. Zeng, J.W. Cai (2023). Improving trial generalizability using observational studies, Biometrics, 79, 1213–1225. [arxiv] [code]

    ** Winner of the 2020 ENAR Distinguished Student Paper Competition Award

  55. [genRCT] D. Lee*, S. Yang+, and X. Wang (2022). Doubly robust estimators for generalizing treatment effects on survival outcomes from randomized controlled trials to a target population. Journal of Causal Inference, 10, 415-440. [arxiv] [code]
  56. [DI:review] S. Yang and X. Wang (2022). RWD-integrated randomized clinical trial analysis. 2022 ASA Biopharmaceutical Report Real World Evidence (Editors: Herbert Pang, Ling Wang, Kristi L. Griffiths), 29, 15–21.
  57. X. Mao, Z. Wang, and S. Yang (2022). Matrix completion under complex survey sampling. Annals of the Institute of Statistical Mathematics, doi:10.1007/s10463-022-00851-5. [arxiv]
  58. C. Gao*, K. J. Thompson, S. Yang and J. K. Kim (2022). Nearest neighbor ratio imputation with incomplete multinomial outcome in survey sampling. Journal of the Royal Statistical Society: Series A, 185, 1903-1930.
  59. J.Y. Wang, R. Wong, S. Yang, and G. Chan (2022). Estimation of Partially Conditional Average Treatment Effect by Hybrid Kernel-covariate Balancing. Electronic Journal of Statistics, doi.org/10.1214/22-EJS2000. [arxiv]
  60. [SpaCI] A. B. Giffin*, B. J. Reich , S. Yang, and A. Rappold (2022). Generalized propensity score approach to causal inference with spatial interference. Biometrics, 79, 2220–2231. [arxiv]

    ** Winner of the 2021 ENAR Distinguished Student Paper Competition Award

  61. [SpaCI] A. B. Giffin*, W. Gong, S. Majumder, A. Rappold, B. J. Reich, and S. Yang (2022). Estimating intervention effects on infectious disease control: the effect of community mobility reduction on Coronavirus spread. Spatial Statistics, 52, 100711. [arxiv]
  62. [Matching] H. Zhao*, X. Zhang and S. Yang (2022). Double score matching in observational studies with multi-level treatments. Communications in Statistics – Simulation and Computation, doi.org/10.1080/03610918.2022.2118778.
  63. H. Zhao* and S. Yang (2022). Outcome-adjusted balance measure for generalized propensity score model selection. Journal of Statistical Planning and Inference, 221, 188–200. [arxiv]

    ** Winner of the 2021 DISS Best Poster Award

  64. [IIOT] [MSM&SNM] D. Johnson*, K. Pieper, and S. Yang+ (2022). Treatment-specific Marginal Structural Cox Model for the Effect of Treatment Discontinuation. Pharmaceutical Statistics, 21, 988-1004.
  65. J. W. Yu, D. Bandyopadhyay, S. Yang, L. Kang, and G. Gupta (2022). Propensity score modeling in electronic health records with time-to-event endpoints: application to kidney transplantation. Journal of Data Science, 20, 188–208.
  66. M.Y. Huang and S. Yang+ (2022). Robust inference of conditional average treatment effects using dimension reduction. Statistica Sinica, 32, 547-567. [arxiv]
  67. [SpaCI] A. Larsen*, S. Yang, A. Rappold, and B. Reich (2022). A spatial causal analysis of wildland fire-contributed PM2.5 using numerical model output. Annuals of Applied Statistics, 16, 2714-2731. [arxiv]
  68. [SpaCI] B. J. Reich, S. Yang, and Y. Guan (2022). Discussion on “Spatial+: a novel approach to spatial confounding” by Dupont, Wood and Augustin, Biometrics, doi.org/10.1111/biom.13651.
  69. [Matching] N. Corder* and S. Yang+ (2022). Utilizing stratified generalized propensity score matching to approximate blocked trial designs with multiple treatment levels. Journal of Biopharmaceutical Statistics, doi:10.1080/10543406.2022.2065507.
  70. [Matching] Y. Zhang*, S. Yang, W. Ye, D. E. Faries, I. Lipkovich, Z. Kadziola (2021). Best practices of double score matching for estimating causal effects, Statistics in Medicine, 42, 1421–1445.
  71. [IIOT] [MSM&SNM] S. Yang (2022). Semiparametric efficient estimation of structural nested mean models with irregularly spaced observations. Biometrics, 78, 937–949. [arxiv]
  72. [SpaCI] B. J. Reich, S. Yang, Y. Guan, A. B. Giffin, M. J. Miller and A. G. Rappold (2021). A review of spatial causal inference methods for environmental and epidemiological applications. International Statistical Review, 89, 605-634. [arxiv]
  73. S. Yang, J. K. Kim, and Youngdeok Hwang (2021). Integration of data from probability surveys and big found data for finite population inference using mass imputation. Survey Methodology, 47, 29–58.
  74. F. Cools, D. Johnson, A. J. Camm, J. P. Bassand, F. Verheugt, S. Yang, A. Tsiatis, D. A. Fitzmaurice, S. Z. Goldhaber, G. Kayani, S. Goto, S. Haas, F. Misselwitz, A. Turpie, K. Fox, K. Pieper, A. K. Kakkar (2021). Risks associated with discontinuation of oral anticoagulation in newly diagnosed patients with atrial fibrillation: results from the GARFIELD-AR Registry. Journal of Thrombosis and Hemostasis, doi:10.1111/jth.15415. (Collaboration work)
  75. S. Yang, J. K. Kim, and R. Song (2020). Doubly robust inference when combining probability and non-probability samples with high-dimensional data, Journal of the Royal Statistical Society: Series B, 82, 445–465.
  76. [IIOT] [MSM&SNM] S. Yang, K. Pieper, and F. Cools (2020). Semiparametric estimation of structural failure time model in continuous-time processes, Biometrika, 107, 123-136.
  77. N. Corder* and S. Yang (2020). Estimating average treatment effects utilizing fractional imputation when confounders are subject to missingness, Journal of Causal Inference, 8, 249-271.
  78. L. Dong*, E. Laber, Y. Goldberg, R. Song, S. Yang (2020). Ascertaining properties of weighting in the estimation of optimal treatment regimes under monotone missingness, Statistics in Medicine, doi: 10.1002/sim.8678.
  79. S. Yang and P. Ding (2020). Combining multiple observational data sources to estimate causal effects, Journal of American Statistical Association, 115, 1540–1554.
  80. S. Yang and J. K. Kim (2020). Statistical data integration in survey sampling: a review, Japanese Journal of Statistics and Data Science, 3, 625–650.
  81. W. Li*, S. Yang,+ and P. Han (2020). Robust estimation for moment condition models with data missing not at random, Journal of Statistical Planning and Inference, 207, 246–254.
  82. [Matching] S. Yang and J. K. Kim (2020). Asymptotic theory and inference of predictive mean matching imputation using a superpopulation model framework. Scandinavian Journal of Statistics, 47, 839–861.
  83. S. Chen, S. Yang, and J.K. Kim (2020). Nonparametric mass imputation for data integration. Journal of Survey Statistics and Methodology, doi.org/10.1093/jssam/smaa036.
  84. S. Yang (2019). Book reviews: Flexible imputation of missing data, 2nd ed. Journal of American Statistical Association, 114, 1421–1421.
  85. S. Yang, L. Wang, and P. Ding (2019). Causal inference with confounders missing not at random, Biometrika, 106, 875–888.
  86. S. Yang and D. Zeng (2018). Discussion on penalized spline of propensity methods for treatment comparison by Zhou, Elliott and Little, Journal of American Statistical Association, 114, 30–32.
  87. [MSM&SNM] S. Yang  and J. J. Lok (2018). Sensitivity analysis for unmeasured confounding in coarse structural nested mean models, Statistica Sinica, 28, 1703–1723.
  88. S. Yang (2018). Propensity score weighting for causal inference with clustered data, Journal of Causal Inference, doi.org/10.1515/jci-2017-0027.
  89. S. Yang and J. K. Kim (2018). Nearest neighbor imputation for general parameter estimation in survey sampling, Advances in Econometrics, 39, 211–236.
  90. S. Yang and P. Ding (2018). Asymptotic inference of causal effects with observational studies trimmed by the estimated propensity scores, Biometrika, 105, 487–493. [arixv]
  91. Z. Wang, J. K. Kim, and S. Yang (2018). An approximate Bayesian inference under informative sampling, Biometrika, 105, 91–102.
  92. J. Lok, S. Yang, B.Sharkey, Hughes, M (2018). Estimation of the cumulative incidence function under multiple dependent and independent censoring mechanisms, Lifetime Data Analysis, 24, 201–223.
  93. [MSM&SNM] S. Yang, A. A. Tsiatis, and M. Blazing (2018). Modeling survival distribution as a function of time to treatment discontinuation: a dynamic treatment regime approach, Biometrics, 74, 900–909.
  94. S. Yang and J. K. Kim (2017). A semiparametric inference to regression analysis with missing covariates in survey data, Statistica Sinica, 27, 261–285.
  95. J. K. Kim and S. Yang (2017). A note on multiple imputation under complex sampling, Biometrika, 104, 221–228.
  96. S. Yang  and J. K. Kim (2017). Discussion: dissecting multiple imputation from a multi-phase inference perspective: what happens when god’s, imputer’s and analyst’s models are uncongenial? by X. Xie and X. L. Meng, Statistica Sinica, 27, 1568–1573.
  97. [MSM&SNM] S. Yang, and J. J. Lok (2016). A goodness-of-fit test for structural nested mean models, Biometrika, 103, 734–741.
  98. S. Yang, and J. K. Kim (2016). Fractional imputation in survey sampling: a comparative review, Statistical Science, 31, 415–432.
  99. [Matching] S. Yang, G. Imbens, Z. Cui, D. Faries and Z. Kadziola (2016), Propensity score matching and stratification in observational studies with multi-level treatments, Biometrics, 72, 1055–1065. With R package available “multilevelMatching“.
  100. S. Yang and J. K. Kim (2016). A note on multiple imputation for method of moments estimation, Biometrika, 103, 244–251.
  101. S. Yang and J. K. Kim (2015). Likelihood-based inference with missing data under missing-at-random, Scandinavian Journal of Statistics, 43, 436–454.

    ** Winner of the 2014 JSM Student Paper Competition Award

  102. L. Peyer, G. Welk, L. B. Davis, S. Yang, and J. K. Kim (2015). Factors associated with parent concern for child weight and parenting behaviors, Childhood Obesity, 11, 269–274. (Collaboration work)
  103. S. Yang and Z. Zhu (2015). Variance estimation and kriging prediction for a class of non-stationary spatial models, Statistica Sinica,25, 135–149.
  104. J. K. Kim and S. Yang (2014). Fractional hot deck imputation for robust estimation under item nonresponse in survey sampling, Survey Methodology, 40, 211–230.
  105. J. K. Kim, Z. Zhu, and S. Yang (2013). Improved estimation for June Area Survey incorporating several information, Proceedings 59th ISI World Statistics Congress, Hong Kong, China, 199–204.
  106. S. Yang, J. K. Kim and D. W. Shin (2013). Imputation methods for quantile estimation under missing at random, Statistics and Its Interface, 6, 369–377.
  107. S. Yang, J. K. Kim and Z. Zhu (2013). Parametric fractional imputation for mixed models with nonignorable missing data, Statistics and Its Interface, 6, 339–347.

 

Submitted papers 

 

  1. A. B. Giffin*, B. J. Reich, S. Yang, and A. Rappold. Instrumental variables, spatial confounding and interference. [arxiv]
  2. S. Yang and Z. Zhu. Semiparametric estimation of spectral density and variogram with irregular observations. [arxiv]
  3. S. Xu*, S. Yang, B. J. Reich. A Bayesian non-parametric method for estimating causal quantile effects.
  4. Y. Zhang, D. Kong, and S. Yang+. Towards R-learner of conditional average treatment effects with a continuous treatment: T-identification, estimation, and inference. [arxiv]

    5. ** Winner of the 2023 ASA Section on Nonparametric Statistics Student Paper Award

  5. [Matching] T. Xu*, Y. Zhang, and S. Yang. Augmented match weighted estimators for average treatment effects.
  6.   Z. Wang, S. Yang and J.K. Kim. Multiple bias-calibration for adjusting selection bias of non-probability samples using data integration. [arxiv]

 

Thesis

 

  • S. Yang (2014). Fractional imputation method of handling missing data and spatial statistics. Iowa State University. [Link]