Google Scholar

Barnard, M., Huling, J.D., and Wolfson, J. (2024+). A Unified Framework for Causal Estimand Selection.
[arxiv:2410.12093]

Clark, J.M., Rott, K.W., Hodges, J.S., and Huling, J.D.. (2024+). Transportability of Principal Causal Effects.
[arxiv:2405.04419]

Wastvedt, S., Huling, J.D., and Wolfson, J. (2023+). Counterfactual fairness for small subgroups.
[arxiv:2310.19988]

Clark, J.M., Rott, K.W., Hodges, J.S., and Huling, J.D.. (2023+). Causally-Interpretable Random-Effects Meta-Analysis.
[arxiv:2302.03544]

Dai, X. and Huling, J.D.. (2021+). Selection and estimation optimality in high dimensions with the TWIN penalty.
[arxiv:1806.01936]

Jiang, Z. and Huling, J.D.. (2023+). Enhancing modified treatment policy effect estimation with weighted energy distance.
The Annals of Applied Statistics, to appear. [pdf] | [arxiv:2310.11620] | [Distinguished Student Paper Award of the International Biometrics Society, ENAR Region, 2024]

Lee, J, Huling, J.D., and Chen, G. (2024). An effective framework for estimating individualized treatment rules with multi-category treatments.
NeurIPS, to appear.

Rott, K.W., Clark, J.M., Murad, M.H., Hodges, J.S., and Huling, J.D. (2024). Causally interpretable meta-analysis combining aggregate and individual participant data.
American Journal of Epidemiology, to appear. doi: 10.1093/aje/kwae371

Huling, J.D., Greifer, N., and Chen, G. (2024). Independence weights for causal inference with continuous treatments.
Journal of the American Statistical Association, 119(546):1657-1670.
[arxiv:2107.07086] | [code] | [CRAN] | doi: 10.1080/01621459.2023.2213485

Huling, J.D. and Mak, S. (2024). Energy Balancing of covariate distributions.
Journal of Causal Inference, to appear.
[arxiv:2004.13962] | doi: 10.1515/jci-2022-0029 | [implemented in Noah Greifer's WeightIt package]

Wastvedt, S., Huling, J.D., and Wolfson, J. (2023). An intersectional framework for counterfactual fairness in risk prediction.
Biostatistics, to appear.
[arxiv:2210.01194] | [code] | doi: 10.1093/biostatistics/kxad021

Chen, R., Huling, J.D., Chen, G., and Yu, M. (2023). Robust sample weighting to facilitate individualized treatment rule learning for a target population.
Biometrika, 111(1):309–329.
[arxiv:2105.00581] | doi: 10.1093/biomet/asad038

Maronge, J.M., Huling, J.D., and Chen, G. (2023). A reluctant additive model framework for interpretable nonlinear individualized treatment rules.
The Annals of Applied Statistics, 17(4):3384-3402.
[pdf] | doi: 10.1214/23-AOAS1767

Huling, J.D., Lundine, J.P., and Leonard, J.C. (2023). Doubly structured sparsity for grouped multivariate responses with application to functional outcome score modeling.
Statistics in Medicine, 42(15):2619-2636.
[pdf] | [code] | doi: 10.1002/sim.9740

Huling, J.D. and Yu, M. (2022). Sufficient dimension reduction for populations with structured heterogeneity.
Biometrics, 78(4):1626-1638.
[pdf] | [code] | [CRAN] | doi: 10.1111/biom.13546

Cheng, J.J., Huling, J.D., and Chen, G. (2022). Meta-analysis of individualized treatment rules via sign-coherency.
Proceedings of the 2nd Machine Learning for Health symposium, PMLR, 193:171-198.
[pdf] | [code] | link: proceedings.mlr.press/v193/cheng22a

Huling, J.D. and Chien, P. (2022). Fast penalized regression and cross validation for tall data with the oem package.
Journal of Statistical Software, 104(6):1-24.
[pdf] | [code] | [CRAN] | [documentation] | doi: 10.18637/jss.v104.i06 | [arxiv:1801.09661]

Huling, J.D. and Yu, M. (2021). Subgroup identification using the personalized package.
Journal of Statistical Software, 98(5):1-60.
[pdf] | [code] | [CRAN] | [documentation] | doi: 10.18637/jss.v098.i05

Yu, M., Kuang, C., Huling, J.D., and Smith, M.A. (2021). Diagnosis-group-specific transitional care program recommendations for thirty-day rehospitalization reduction.
The Annals of Applied Statistics, 15(3):1478-1498.
[pdf] | [code] | doi: 10.1214/21-AOAS1473

Huling, J.D., Smith, M.A., and Chen, G. (2021). A two-part framework for estimating individualized treatment rules from semi-continuous outcomes.
Journal of the American Statistical Association, 116(533):210-223.
[code] | [CRAN] | [documentation] | doi: 10.1080/01621459.2020.1801449

Huling, J.D., Yu, M., and O'Malley, A.J. (2019). Instrumental variable based estimation under the semiparametric accelerated failure time model.
Biometrics, 75(2):516-527.
[pdf] | [code] | doi: 10.1111/biom.12985

Huling, J.D., Yu, M., and Smith, M.A. (2019). Fused comparative intervention scoring for heterogeneity of longitudinal intervention effects. The Annals of Applied Statistics, 13(2): 824-847.
[pdf] | [code] | doi: 10.1214/18-AOAS1216

Huling, J.D., Yu, M., Liang, M., and Smith, M.A. (2018). Risk prediction for heterogeneous populations with application to hospital admission prediction. Biometrics, 74(2): 557-565.
[code] | [CRAN] | [documentation] | doi: 10.1111/biom.12769

Nie, X., Huling, J.D., and Qian, P.Z.G. (2017). Accelerating large-scale statistical computation with the GOEM algorithm. Technometrics, 59(4): 416-425.
doi: 10.1080/00401706.2016.1256840

Xiong, S., Dai, B, Huling, J.D., and Qian, P.Z.G. (2016). Orthogonalizing EM: A design-based least squares algorithm. Technometrics, 58(3): 285-293.
[code] | [CRAN] | [documentation] | [Julia code] | doi: 10.1080/00401706.2015.1054436