Google Scholar Page
A complete list of publications can be found on my CV

Selected Publications

  1. Cheng C, Liu B, Wruck L, Li F, F Li. (2025). Multiply robust estimation for causal survival analysis with treatment noncompliance. Annals of Applied Statistics, forthcoming. [arxiv]
  2. Papadogeorgou G, Liu B, Li F, Li F. (2025). Addressing selection bias in cluster randomized experiments via weighting.Biometrics, 81(1) [arxiv]
  3. Zhao A, Ding P, Li F. (2024). Covariate adjustment of randomized experiments with missing outcomes and covariates. Biometrika, 114(1), 1413-1420. [arxiv] [slides]
  4. Liu B, Wruck L, Li, F. (2024). Principal stratification analysis of noncompliance with time-to-event outcomes. Biometrics, 80(1), 1-14.[arxiv]
  5. Zeng S, Li F, Hu L, Li F. (2023). Propensity score weighting for survival outcomes using pseudo observations. Statistica Sinica. 33, 2161-2184 [arxiv]
  6. Song Y, Chang CR, Li F, Wang R. (2023). Covariate adjustment in randomized experiments with incomplete covariate and outcome data. Statistics in Medicine. 42(22), 3919-3935. [arxiv]
  7. Yang S, Zhou R, Li, F, Thomas LE. (2023). Propensity Score Methods for Causal Subgroup Analysis with Time-to-Event Outcomes. Statistical Methods in Medical Research. 32(10):1919-1935.
  8. Lange E, Zeng S, Campos F, Li F, Tung J, Archie E, Alberts S. (2023). Early life adversity and adult social relationships have independent effects on survival in a wild animal model of aging. Science Advances. 9, eade717 [bioRxiv]
  9. Li F, Ding P, Mealli, F. (2023). Bayesian causal inference: a critical review. Philosophical Transactions of the Royal Society A. 381: 2022.0153. [arxiv]
  10. Zeng S, Lange E, Campos F, Archie E, Alberts S, Li F. (2023). A Causal Mediation Model for Longitudinal Mediators and Survival Outcomes with an Application to Animal Behavior. Journal of Biological, Environmental and Agricultural Statistics. 28, 197-218.[arxiv].
  11. Papadogeorgou G, Imai K, Lyall J, Li F. (2022) Causal inference with spatio-temporal data: Evaluating the effects of airstrikes on insurgent violence in Iraq. Journal of Royal Statistical Society Series B. 84(5), 1969-1999. [arxiv]
  12. Makinen T, Li F, Mercatanti A, Silvestrini A. (2022). Effects of eligibility for central bank purchases on corporate bond spreads. Economic Modeling. 113, 105873. [PDF]
  13. Wang Z, Akande O, Poulos J, Li F. (2022). Are deep learning models superior for missing data imputation in complex surveys?: Evidence from an empirical comparison. Survey Methodology 48(2), 375-399. [arxiv]
  14. Zhou T, Tong G, Li F, Thomas LE, Li F. (2022). PSweight: An R package for propensity score weighting analysis. The R Journal. [arxiv][R vignette]
  15. Cheng C, Li F, Thomas LE, Li F. (2022). Addressing extreme propensity scores in estimating counterfactual survival functions via the overlap weights. American Journal of Epidemiology. 191(6), 1140-1151. [arxiv]
  16. Li F, Tian Z, Bobb J, Papadogeorgou G, Li F. (2021). Clarifying selection bias in cluster randomized trials. Clinical Trials. 19(1), 33-41. [arxiv]
  17. Yang S, Li F, Thomas LE, Li F. (2021). Covariate adjustment in subgroup analyses of randomized clinical trials: A propensity score approach. Clinical Trials. 18(5). 570–581. [arxiv]
  18. Yang S, Lorenzi E, Papadogeorgou G, Wojdyla D, Li F, Thomas LE. (2021). Propensity score weighting for causal subgroup analysis. Statistics in Medicine. 40:4294-4309. [arxiv]
  19. Zeng S, Rosenbaum S, Archie EA, Alberts SC, Li, F. (2021). Causal mediation analysis for sparse and irregular longitudinal data. Annals of Applied Statistics. 15(2), 747-767. [arxiv]
  20. Li, F, A. Mercatanti, T. Makinen, A. Silvestrini. (2021). A regression discontinuity design for ordinal running variable: Evaluating Central Bank purchases of corporate bonds. Annals of Applied Statistics. 15(1), 304-322. [arxiv]
  21. Zeng, S, Li, F, Wang R, Li, F. (2021). Propensity score weighting for covariate adjustment in randomized clinical trials. Statistics in Medicine. 40(4), 842-858. [DOI|arxiv]
  22. Zeng, S, Li, F, Ding, P. (2020). Is being an only child harmful to psychological health? Evidence from a local instrumental variable analysis of the China One-Child Policy. Journal of Royal Statistical Society - Series A. 183(4): 1615-1635. [DOI|arXiv]
  23. Rosenbaum, S, Zeng, S, Campos, FA, Gesquiere, LR, Altmann, J, Alberts, SC, Li, F, Archie, EA. (2020). Social bonds do not mediate the relationship between early adversity and adult glucocorticoids in wild baboons. Proceedings of the National Academy of Sciences. 117(33): 20052-20062. [DOI]
  24. Thomas, LE, Li, F, Pencina, M. (2020). Overlap weighting: a propensity score method that mimics attributes of a randomized clinical trial. Journal of American Medical Association. 323(23):2417-2418. [DOI]
  25. Thomas, LE, Li, F, Pencina, M. (2020). Using propensity score methods to create target populations in observational clinical research. Journal of American Medical Association. 323(5):466-467. [DOI]
  26. Dong, J, Zhang, J, Zeng, S, Li, F. (2020). Subgroup balancing propensity score. Statistical Methods in Medical Research. 29(3) 659–676. [DOI | PDF]
  27. Li, F, and Li, F. (2019). Propensity score weighting for causal inference with multiple treatments. Annals of Applied Statistics. 13(4), 2389-2415. [arXiv][supplement and code]
  28. Ding, P, and Li, F. (2019). A bracketing relationship between difference-in-differences and lagged-dependent-variable adjustment. Political Analysis. 27(4), 605-615. [DOI | arXiv]
  29. Li, F, and Li, F. (2019). Double-robust estimation in difference-in-differences with an application to traffic safety evaluation. Observational Studies. 5, 1-20. [PDF]
  30. Li, F, Thomas, LE, and Li, F. (2019). Addressing extreme propensity scores via the overlap weights. American Journal of Epidemiology. 188(1), 250-257. [DOI]
  31. Ding, P, and Li, F. (2018). Causal inference: a missing data perspective. Statistical Science. 33(2), 214-237. [DOI | arXiv]
  32. Li, F, Morgan, KL, and Zaslavsky, AM. (2018). Balancing covariates via propensity score weighting. Journal of the American Statistical Association. 113(521), 390-400. [DOI | arXiv]
  33. Mercatanti, A, and Li, F. (2017). Do debit cards decrease cash demands?: Causal inference and sensitivity analysis using Principal Stratification. Journal of Royal Statistical Society - Series C (Applied Statistics). 66(4), 759-776. [DOI | arXiv]
  34. Akande, O, Li, F, and Reiter, JP. (2017). An empirical comparison of multiple imputation methods for categorical data. American Statistician. 71(2), 162-170. [DOI]
  35. Li, F, Mattei, A, and Mealli, F. (2015). Evaluating the causal effect of university grants on student dropout: Evidence from a regression discontinuity design using Principal Stratification. Annals of Applied Statistics. 9(4), 1906-1931. [DOI | arXiv]
  36. Li, F, Zhang, T, Wang, Q, Gonzalez, M, Maresh, E, and Coan, JA. (2015). Spatial Bayesian Variable Selection and Grouping in High-dimensional Scalar-on-Image Regressions. Annals of Applied Statistics. 9(2), 687-713. [DOI | arXiv]
  37. Zhang, T, Wu, J, Li, F, Boatman-Reich, D, and Caffo, B. (2015). A Directional dynamic model for effective brain connectivity using electrocorticographic (ECoG) time series. Journal of the American Statistical Association. 110(509), 93-106. [DOI]
  38. Mercatanti, A, Li, F, and Mealli, F. (2014). Improving inference of Gaussian mixtures using auxiliary variables. Statistical Analysis and Data Mining. 8(1), 34-48. [DOI | arXiv]
  39. Mercatanti, A, and Li, F. (2014). Do debit cards increase household spending? Evidence from a semiparametric causal analysis of a survey. Annals of Applied Statistics. 8(4), 2405-2508. [DOI]
  40. Li, F, and Mealli, F. (2014). A Conversation with Donald B. Rubin. Statistical Science. 29(3), 439-457. [DOI]
  41. Li, F, Baccini, M, Mealli, F, Zell, EZ, Frangakis, CE, and Rubin, DB. (2014). Multiple imputation by ordered monotone blocks with application to the Anthrax Vaccine Adsorbed Trial. Journal of Computational and Graphical Statistics. 23(3), 877-892. [DOI]
  42. Mattei, A, Li, F, and Mealli, F. (2013). Exploiting multiple outcomes in Bayesian principal stratification analysis with application to the evaluation of a job training program. Annals of Applied Statistics . 7(4), 2336-2360. [DOI]
  43. Li, F, Zaslavsky, AM, and Landrum, MB. (2013). Propensity score weighting with multilevel data. Statistics in Medicine. 32(19), 3373-3387. [DOI] [slides]
  44. Schwartz, SL, Li, F, and Reiter, JP. (2012). Sensitivity analysis for unmeasured confounding in principal stratification. Statistics in Medicine, 31(10), 949-962. [DOI] [supplement]
  45. Schwartz, SL, Li, F, and Mealli, F. (2011). A Bayesian semiparametric approach to intermediate variables in causal inference. Journal of the American Statistical Association, 106(496), 1331-1344. [DOI] [supplement] [talk]
  46. Li, F, and Zaslavsky, AM. (2010). Using a short screening scale for small-area estimation of prevalence of mental illness prevalence for schools. Journal of the American Statistical Association, 105(492), 1323-1332. [DOI]
  47. Li, F and Zhang, NR. (2010). Bayesian variable selection in structured high-dimensional covariate spaces with applications in genomics. Journal of the American Statistical Association, 105(491), 1202-1214. [DOI] [code]
  48. Li, F, and Frangakis, CE (2006). Polydesigns and causal inference. Biometrics, 62(2), 343-51. [DOI]

    49. Preprints

  49. Liu B, Li F. (2025). Sample size and power calculations for causal inference in observational studies. arXiv:2501.11181 [arxiv]
  50. Zhao A, Ding P, Li F. (2025). Interacted two-stage least squares with treatment effect heterogeneity. arXiv:2502.00251 [arxiv]
  51. Liu B, Li F. (2023). PStrata: A R package for principal stratification analysis. arXiv:2304.02740 [arxiv]
  52. Li, F, Yu, Y and Rubin, DB. (2012). Imputing missing data by fully conditional models: some cautionary examples and guidelines. Duke University Department of Statistical Science Discussion Paper 11-24. [PDF]