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A complete list of publications can be also found on my CV

Publications: Statistical Methods

  1. Yang S, Li F, Thomas LE, Li F. (2021). Covariate adjustment in subgroup analyses of randomized clinical trials: A propensity score approach. Clinical Trials. Forthcoming. arXiv:2102.02285 [arxiv]
  2. Yang S, Lorenzi E, Papadogeorgou G, Wojdyla D, Li F, Thomas LE. (2020). Propensity score weighting for causal subgroup analysis. Statistics in Medicine. Forthcoming. arXiv:2010.02121. [arxiv]
  3. Assaad S, Zeng S, Tao C, Datta S, Mehta N, Henao R, Li, F, Carin L. (2021). Counterfactual representation learning with balancing weights. International Conference on Artificial Intelligence and Statistics 2021 (AISTAT). Forthcoming. [arxiv]
  4. Zeng S, Rosenbaum S, Archie EA, Alberts SC, Li, F. (2021). Causal mediation analysis for sparse and irregular longitudinal data. Annals of Applied Statistics. Forthcoming. arXiv:2007.01796. [arxiv]
  5. 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), 1-19. [arxiv]
  6. 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]
  7. Lu D, Tao C, Chen J, Li, F, Guo F, Carin L. (2020). Reconsidering generative objectives for counterfactual reasoning. Advances in Neural Information Processing Systems (NeurIPS 2020). [pdf]
  8. 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]
  9. 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]
  10. 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]
  11. 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]
  12. Lu, D, Guo, F, Li, F. (2020). Evaluating the causal effects of cellphone distraction on crash risk using propensity score methods. Accident Analysis and Prevention. 143, 105579. [DOI]
  13. Dong, J, Zhang, J, Zeng, S, Li, F. (2020). Subgroup balancing propensity score. Statistical Methods in Medical Research. 29(3) 659–676. [DOI | PDF]
  14. 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]
  15. 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]
  16. 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]
  17. 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]
  18. Wang, F, Wang, J, Gelfand, AE, and Li, F. (2019). Disease mapping with generative models. American Statistician. 73(3), 212-223. [DOI]
  19. Ding, P, and Li, F. (2018). Causal inference: a missing data perspective. Statistical Science. 33(2), 214-237. [DOI | arXiv]
  20. 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]
  21. Wang, F, Wang, J, Gelfand, AE, and Li, F. (2017). Accommodating the ecological fallacy in disease mapping in the absence of individual exposures. Statistics in Medicine. 36, 4930-4942. [DOI]
  22. 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]
  23. 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]
  24. 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]
  25. 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]
  26. 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]
  27. 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]
  28. Schliep, EM, Dong, Q, Gelfand, AE, and Li, F. (2014). Modeling individual tree growth fusing diameter tape and increment core data. Environmetrics. 25(8), 610-620. [DOI]
  29. 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]
  30. Li, F, and Mealli, F. (2014). A Conversation with Donald B. Rubin. Statistical Science. 29(3), 439-457. [DOI]
  31. 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]
  32. Zhang, T, Li, F, Gonzalez, M, Maresh, E, and Coan, J.A. (2014). A semi-parametric nonlinear functional model for fMRI data. NeuroImage. 97, 178-187. [DOI]
  33. Liu, F, Chakraborty, S, Li, F, Liu, Y, and Lozano, AC. (2014). Bayesian regularization via Graph Laplacian. Bayesian Analysis . 9(2), 449-474. [DOI]
  34. 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]
  35. Li, F, Zaslavsky, AM, and Landrum, MB. (2013). Propensity score weighting with multilevel data. Statistics in Medicine. 32(19), 3373-3387. [DOI] [slides]
  36. Zhang, T, Li, F, Beckes, L, and Coan, JA. (2013). A semi-parametric model for the hemodynamic response for multi-subject fMRI data. NeuroImage, 75, 136-145. [DOI]
  37. Zhang, T, Li, F, Becks, L, Brown, C, and Coan, JA. (2012). Nonparametric inference of hemodynamic response for multi-subject fMRI data under multi-stimulus designs. NeuroImage, 63(3), 1754-1765. [DOI] [supplement]
  38. 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]
  39. 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]
  40. Mealli, F and Li, F. (2011). Discussion of "Transparent parametrization of models for potential outcomes" by Richardson, Evans and Robins. Bayesian Statistics 9 (J.M. Bernardo, M.J. Bayarri, J.O. Berger, A.P. Dawid, D. Heckerman, A.F.M. Smith and M. West eds.). Oxford University Press. [PDF]
  41. 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]
  42. 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]
  43. Li, F, Green, JG, Zaslavsky, AM, and Kessler, R. (2010). Estimating prevalence of serious emotional disturbance in schools using a brief screening scale. International Journal of Methods in Psychiatric Research, 19 (Supplement 1), 88-98. [DOI]
  44. Baccini, M, Cook, S, Frangakis, CE, Li, F, Mealli, F, Rubin, DB, and Zell EZ. (2010). Multiple imputation in the Anthrax Vaccine Research Program. Chance, 23(2), 16-23. [DOI]
  45. Li, F, and Frangakis, CE (2006). Polydesigns and causal inference. Biometrics, 62(2), 343-51. [DOI]
  46. Li, F, and Frangakis, CE (2005). Designs for partially controlled studies: Messages from a review. Statistical Methods in Medical Research, 14, 417-431. [DOI]

    Discussions

  47. Papadogeorgou, G, and Li, F. (2020). Discussion of ``Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects'' by Hahn, Murray and Carvalho. Bayesian Analysis. 15(3): 1007-1013.
  48. Papadogeorgou, G, and Li, F. (2019). Discussion of ``Penalized spline of propensity methods for treatment comparison'' by Zhou, Elliot and Little. Journal of the American Statistical Association. 114(525):32-35. [DOI]
  49. Mealli, F and Li, F. (2011). Discussion of "Transparent parametrization of models for potential outcomes" by Richardson, Evans and Robins. Bayesian Statistics 9 (J.M. Bernardo, M.J. Bayarri, J.O. Berger, A.P. Dawid, D. Heckerman, A.F.M. Smith and M. West eds.). Oxford University Press. [PDF]

    Preprints

  50. Papadogeorgou, G, Imai, K, Lyall, J, Li, F. (2020) Causal inference with spatio-temporal data: Evaluating the effects of airstrikes on insurgent violence in Iraq. arXiv:2003.13555 [arxiv]
  51. Zeng S, Tao C, Assaad S, Carin L, Li F. (2020). Double-robust representation learning for causal inference. arXiv:2010.07866. [arxiv]
  52. Zeng S, Li F, Hu L, Li F. (2021). Propensity score weighting for survival outcomes using pseudo observations. arXiv:2103.00605. [arxiv]
  53. Zhou T, Tong G, Li F, Thomas LE, Li F. (2020). PSweight: An R package for propensity score weighting analysis. arXiv: 2010.08893. [arxiv]
  54. Wang Z, Akande O, Poulos J, Li F. (2021). Are deep learning models superior for missing data imputation in complex surveys?: Evidence from an empirical comparison. arXiv: 2103.09316. [arxiv]
  55. T. Makinen, Li F, Mercatanti A, Silvestrini A. (2021). Effects of eligibility for central bank purchases on corporate bond spreads. Bank for International Settlements (BIS) Working Papers. No 894. [PDF]
  56. 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]