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Jared Murray

Assistant Professor

Department:     Information, Risk & Operations Management

Research Areas:     Bayesian Methods, Causal Inference

Jared S. Murray is an assistant professor of information, risk, and operations management at The University of Texas at Austin’s McCombs School of Business. He is also an assistant professor of statistics and data science within the College of Natural Sciences. Before 2017, Murray was a visiting assistant professor in the Department of Statistics at Carnegie Mellon University.

His research involves developing flexible Bayesian multivariate models for heterogeneous and structured data, with applications to multiple imputation for missing data, latent variable modeling, and causal inference.

Murray recently was awarded a National Science Foundation grant, “Improving Probabilistic Record Linkage and Subsequent Inference,” to develop new methods for matching records across files in the absence of unique identifiers, and for making inference using the combined files.

The NSF also awarded him a Faculty Early Career Development Program award from the Division of Social and Economic Sciences. Murray will serve as principal investigator for the project titled, "Bayesian Tree Models for Next-Generation Studies in the Behavioral and Social Sciences."

Murray served as secretary/treasurer of the American Statistical Association’s Statistical Computing Section in 2018-2019 and was a committee member for the National Academy of Science in 2016-2017.

He also serves as a reviewer for numerous professional journals.

With Ph.D. and M.S. degrees in statistical science from Duke University, Murray also holds a B.S. in interdisciplinary mathematics (statistics) from the University of New Hampshire.




National Science Foundation CAREER Award



ISBA Young Researcher Travel Grant



Oxford University Press OUP-EFaB Research Prize



AISTATS Notable Paper Award



ISBA Young Researcher Travel Grant



Jared Murray, Joseph Pane, Rebecca Nugent, Shengping Yang, and Kenneth Nugent. Electronic Cigarette Use by and Perceptions of Middle and High School Students in the United States. Journal of Investigative Medicine.  Forthcoming. 

Yinpu Li, Antonio R. Linero, and Jared S. Murray.  Sep 2023.
Adaptive Conditional Distribution Estimation with Bayesian Decision Tree Ensembles.

Journal of the American Statistical Association 118(543): 2129-2142.

Elizabeth Tipton, Christopher Bryan, Jared S. Murray, Mark McDaniel, Barbara Lynn Schneider, and David S. Yeager. Mar/Apr 2023. Why Meta-Analyses of Growth Mindset and Other Interventions Should Follow Best Practices for Examining Heterogeneity. Psychological Bulletin 149(3-4): 229-241.

Henrique Bolfarine, Carlos M. Carvalho, Hedibert F. Lopes, and Jared S. Murray. 2022. Decoupling Shrinkage and Selection in Gaussian Linear Factor Analysis. Bayesian Analysis Advance Publication 1-23.

Yeager, David S.; Carroll, Jamie M.; Buontempo, Jenny; Cimpian, Andrei; Woody, Spencer; Crosnoe, Robert; Muller, Chandra; Murray, Jared; Teacher Mindsets Help Explain Where a Growth-Mindset Intervention Does and Doesn't Work. Psychological Science (“Other Disciplinary”). Jan2022, Vol. 33 Issue 1, p18-32.

Jared Murray, Carlos Carvalho, Avi Feller, Spencer Woody, and David Yeager. Assessing Treatment Effect Variation in Observational Studies: Results from a Data Challenge. Observational Studies, forthcoming.


Jared Murray. 2021. Log-Linear Bayesian Additive Regression Trees for Multinomial Logistic and Count Regression Models. Journal of the American Statistical Association 116(534), 756-769.


Neil A. Spencer and Jared Murray. 2020. A Bayesian Hierarchical Model for Evaluating Forensic Footwear Evidence. Annals of Applied Statistics14(3), 1449-1470.


Jennifer E. Starling, Jared Murray, and Carlos Carvalho. 2020. BART with Targeted Smoothing: An Analysis of Patient-specific Stillbirth Risk. Annals of Applied Statistics 14(1), 28-50.


Jared Murray, Carlos Carvalho, and David Yeager. 2019. A National Experiment Reveals Where a Growth Mindset Improves Achievement. Nature 573, 364-369.


Y. Li, A.R. Linero, and J.S. Murray. 2022. Adaptive Conditional Distribution Estimation With Bayesian Decision Tree Ensembles. Journal of the American Statistical Association (to appear).

J.E. Starling, J.S. Murray, P.A. Lohr, A.R.A. Aiken, C.M. Carvalho, and J.G. Scott. 2021. Targeted Smooth Bayesian Causal Forests: An Analysis Of Heterogeneous Treatment Effects for Simultaneous vs. Interval Medical Abortion Regimens Over Gestation. The Annals of Applied Statistics 15 (3), 1194-1219.

S. Woody, C.M. Carvalho, and J.S. Murray. 2021 Model Interpretation Through Lower- Dimensional Posterior Summarization. Journal of Computational and Graphical Statistics 30 (1), 144-161.

P.R. Hahn, J.S. Murray, and C.M. Carvalho. 2020. Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects (with discussion). Bayesian Analysis 15 (3), 965-1056.

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