Jared S. Murray

My current research interests are in developing flexible Bayesian models for heterogenous and structured data, with applications to causal inference, record linkage, multiple imputation for missing data, and latent variable modeling.


1.  Bolfarine*, H., C. M. Carvalho, H. F. Lopes, and J. S. Murray (2020). Decoupling Shrinkage and Selection in Gaussian Linear Factor Analysis. arXiv: 2006.11908 [stat.ME].

2.  Li*, Y., A. R. Linero, and J. S. Murray (2020). Adaptive Conditional Distribution Estimation with Bayesian Decision Tree Ensembles. arXiv: 2005.02490 [stat.ME].

3.  Woody*, S., C. M. Carvalho, P. R. Hahn, and J. S. Murray (2020). Estimating heterogeneous effects of continuous exposures using Bayesian tree ensembles: revisiting the impact of abortion rates on crime. arXiv: 2007.09845 [stat.AP].

4.  Woody*, S., C. M. Carvalho, and J. S. Murray (2020a). Bayesian inference for treatment effects under nested subsets of controls. arXiv: 2001.07256 [stat.ME].

5.  McVeigh*, B. S., B. T. Spahn, and J. S. Murray (2019). Scaling Bayesian Probabilistic Record Linkage with Post-Hoc Blocking: An Application to the California Great Registers. arXiv: 1905.05337 [stat.ME].

6.  Starling*, J. E., C. E. Aiken, J. S. Murray, A. Nakimuli, and J. G. Scott (2019). Monotone function estimation in the presence of extreme data coarsening: Analysis of preeclampsia and birth weight in urban Uganda. arXiv: 1912.06946 [stat.AP].

7.  Starling*, J. E., J. S. Murray, P. A. Lohr, A. R. A. Aiken, C. M. Carvalho, and J. G. Scott (2019). Targeted Smooth Bayesian Causal Forests: An analysis of heterogeneous treatment effects for simultaneous versus interval medical abortion regimens over gestation. arXiv: 1905.09405 [stat.AP].


1.  Murray, J. S. (2020+). Log-Linear Bayesian Additive Regression Trees for Multinomial Logistic and Count Regression Models. Journal of the American Statistical Association, (to appear). arXiv: 1701.01503 [stat.ME].

2.  Spencer*, N. A. and J. S. Murray (2020+). A Bayesian Hierarchical Model for Evaluating Forensic Footwear Evidence. Annals of Applied Statistics, (to appear). arXiv: 1906.05244 [stat.AP].

3.  Hahn, P. R., J. S. Murray, and C. M. Carvalho (2020). Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects (with discussion). Bayesian Analysis. Advance publication.

4.  Hill, J., A. Linero, and J. Murray (2020). Bayesian Additive Regression Trees: A Review and Look Forward. Annual Review of Statistics and Its Application 7(1), 251–278.

5.  Starling*, J. E., J. S. Murray, C. M. Carvalho, R. K. Bukowski, and J. G. Scott (2020). BART with targeted smoothing: An analysis of patient-specific stillbirth risk. Ann. Appl. Stat. 14(1), 28–50.

6.  Woody*, S., C. M. Carvalho, and J. S. Murray (2020b). Model interpretation through lower-dimensional posterior summarization. Journal of Computational and Graphical Statistics 0(ja), 1–34.

7.  Carvalho, C., A. Feller, J. S. Murray, S. Woody*, and D. Yeager (2019). Assessing Treatment Effect Variation in Observational Studies: Results from a Data Challenge. Observational Studies 5, 21–35.

8.  Dalmasso*, N., R. Mejia, J. Rodu, M. Price, and J. Murray (2019). Feature Engineering for Entity Resolution with Arabic Names: Improving Estimates of Observed Casualties in the Syrian Civil War. Artificial Intelligence for Humanitarian Assistance and Disaster Response Workshop, NeurIPS.

9.  Yeager, D. S., P. Hanselman, G. M. Walton, J. S. Murray, R. Crosnoe, C. Muller, E. Tipton, B. Schneider, C. S. Hulleman, C. P. Hinojosa, D. Paunesku, C. Romero, K. Flint, A. Roberts, J. Trott, R. Iachan, J. Buontempo, S. M. Yang, C. M. Carvalho, P. R. Hahn, M. Gopalan, P. Mhatre, R. Ferguson, A. L. Duckworth, and C. S. Dweck (2019). A national experiment reveals where a growth mindset improves achievement. Nature 573(7774), 364–369.

10.  Murray, J. S. (2018). Multiple Imputation: A Review of Practical and Theoretical Findings. Statistical Science 33(2), 142–159.

11.  McVeigh*, B. S. and J. S. Murray (2017b). Scalable Bayesian Record Linkage. Advances in Approximate Bayesian Inference Workshop, NIPS.

12.  Hahn, P. R., J. S. Murray, and I. Manolopoulou (2016). A Bayesian Partial Identification Approach to Inferring the Prevalence of Accounting Misconduct. Journal of the American Statistical Association 111(513), 14–26.

13.  Muray, J. S. (2016a). Bayesian factor analysis in R: Gaussian, probit and Gaussian copula factor modeling with bfa. ISBA Bulletin 23(3), 11–14.

14.  Muray, J. S. (2016b). Review of “Bayesian Statistics for the Social Sciences”. Journal of the American Statistical Association 111(513), 440.

15.  Murray, J. S. and J. P. Reiter (2016). Multiple Imputation of Missing Categorical and Continuous Values via Bayesian Mixture Models With Local Dependence. Journal of the American Statistical Association 111(516), 1466–1479.

16.  Murray, J. S. (2015). Probabilistic Record Linkage and Deduplication after Indexing, Blocking, and Filtering. Journal of Privacy and Confidentiality 7(1).

17.  Banerjee, A., J. Murray, and D. Dunson (2013). Bayesian learning of joint distributions of objects. In: Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics. Ed. by C. M. Carvalho and P. Ravikumar. Vol. 31. Proceedings of Machine Learning Research. Scottsdale, Arizona, USA: PMLR, pp.1–9.

18.  Henao, R., J. Murray, G. Ginsburg, L. Carin, and J. E. Lucas (2013). Patient Clustering with Uncoded Text in Electronic Medical Records. In: AMIA Annual Symposium Proceedings. Vol. 2013. American Medical Informatics Association, pp.592.

19.  Murray, J. S., D. B. Dunson, L. Carin, and J. E. Lucas (2013). Bayesian Gaussian copula factor models for mixed data. Journal of the American Statistical Association 108(502), 656–665.

Technical Reports

1.  Hahn, P. R., V. Dorie, and J. S. Murray (2019). Atlantic Causal Inference Conference (ACIC) Data Analysis Challenge 2017. arXiv: 1905.09515 [stat.ME].

2.  McVeigh*, B. S. and J. S. Murray (2017a). Practical Bayesian Inference for Record Linkage. arXiv: 1710.10558 [stat.ME].


Murray, J. S. (2013). Some Recent Advances in Non- and Semiparametric Bayesian Modeling with Copulas, Mixtures, and Latent Variables