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

Selected Publications

  1. Zhao A, Ding P, Li F. (2024). Covariate adjustment of randomized experiments with missing outcomes and covariates. Biometrika, forthcoming. [arxiv] [slides]
  2. Liu B, Wruck L, Li, F. (2024). Principal stratification analysis of noncompliance with time-to-event outcomes. Biometrics, 80(1), 1-14.[arxiv]
  3. Zeng S, Li F, Hu L, Li F. (2023). Propensity score weighting for survival outcomes using pseudo observations. Statistica Sinica. 33, 2161-2184 [arxiv]
  4. 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]
  5. 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.
  6. 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]
  7. Li F, Ding P, Mealli, F. (2023). Bayesian causal inference: a critical review. Philosophical Transactions of the Royal Society A. 381: 2022.0153. [arxiv]
  8. 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].
  9. 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]
  10. 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]
  11. 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]
  12. 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]
  13. 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]
  14. 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]
  15. 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]
  16. 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]
  17. 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]
  18. 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]
  19. 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]
  20. 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]
  21. 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]
  22. 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]
  23. 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]
  24. Dong, J, Zhang, J, Zeng, S, Li, F. (2020). Subgroup balancing propensity score. Statistical Methods in Medical Research. 29(3) 659–676. [DOI | PDF]
  25. 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]
  26. 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]
  27. 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]
  28. 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]
  29. Ding, P, and Li, F. (2018). Causal inference: a missing data perspective. Statistical Science. 33(2), 214-237. [DOI | arXiv]
  30. 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]
  31. 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]
  32. 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]
  33. 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]
  34. 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]
  35. 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]
  36. 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]
  37. 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]
  38. Li, F, and Mealli, F. (2014). A Conversation with Donald B. Rubin. Statistical Science. 29(3), 439-457. [DOI]
  39. 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]
  40. 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]
  41. Li, F, Zaslavsky, AM, and Landrum, MB. (2013). Propensity score weighting with multilevel data. Statistics in Medicine. 32(19), 3373-3387. [DOI] [slides]
  42. 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]
  43. 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]
  44. 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]
  45. 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]
  46. Li, F, and Frangakis, CE (2006). Polydesigns and causal inference. Biometrics, 62(2), 343-51. [DOI]

    47. Preprints

  47. Papadogeorgou G, Liu B, Li F, Li F. (2023). Addressing selection bias in cluster randomized experiments via weighting. arXiv:2309.07365. [arxiv]
  48. Cheng C, Guo G, Liu B, Wruck L, Li, F, F Li. (2023). Multiply robust estimation for causal survival analysis with treatment noncompliance. arXiv:2305.13443. [arxiv]
  49. Zeng S, Tao C, Assaad S, Carin L, Li F. (2021). Double-robust representation learning for causal inference. arXiv:2010.07866. [arxiv]
  50. Chen J, Gan Z, et al., Li F, Carin L, Tao C. (2021) Simpler, Faster, Stronger: Breaking The log-K Curse On Contrastive Learners With FlatNCE. arXiv:2107.01152 [arxiv]
  51. 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]