My research spans several interconnected areas in statistics and biostatistics, with a unifying focus on developing and using state-of-the-art statistical methods for complex observational studies to improve individual health outcomes. Below are the main themes of my work, with key publications from each area.

Causal Inference

Development of principled methods for estimating causal effects from observational data. Topics include weighting methods for continuous, binary, and nonstandard treatments, modified treatment policies, handling violations of positivity and other standard causal assumptions, methods for generalizability and transportability, handling treatment nonadherence, estimating heterogeneous treatment effects, and more.

Weighting Methods for Causal Inference

A central challenge in observational studies is confounding: because treatment is not randomly assigned, differences in outcomes across treatment groups may reflect pre-existing differences in patient characteristics rather than true causal effects. Weighting methods address this by reweighting the observed sample so that covariate distributions are balanced across groups, enabling valid causal comparisons. Classical propensity score weighting approaches can be sensitive to model misspecification. I develop weighting approaches that are flexible, user-friendly, and robust to complex confounding. This includes energy balancing weights, a model-free method that directly minimizes distributional imbalance and requires no tuning parameters or secondary modeling decisions, as well as independence weights for continuous-valued treatments, modified treatment policy estimators based on weighted energy distance, and more.

  1. A Framework for Causal Estimand Selection Under Positivity Violations Barnard, Martha, Huling, Jared D., and Wolfson, Julian Biometrics (2026) [cite] [arXiv:2410.12093] [doi]
  2. Partially Retargeted Balancing Weights for Causal Effect Estimation Under Positivity Violations Barnard, Martha, Huling, Jared D., and Wolfson, Julian Preprint (2025)
    ★ Distinguished Student Paper Award, ENAR 2026
    [cite] [arXiv:2510.22072]
  3. Data adaptive covariate balancing for causal effect estimation for high dimensional data De, Simion, and Huling, Jared D. Preprint (2025) [cite] [arXiv:2512.18069] [code]
  4. Modified treatment policy effect estimation with weighted energy distance Jiang, Ziren, and Huling, Jared D. The Annals of Applied Statistics, 19(4):2555–2577 (2025)
    ★ Distinguished Student Paper Award, ENAR 2024
    [cite] [arXiv:2310.11620] [doi]
  5. Independence weights for causal inference with continuous treatments Huling, Jared D., Greifer, Noah, and Chen, Guanhua Journal of the American Statistical Association, 119(546):1657–1670 (2024) [cite] [arXiv:2107.07086] [doi] [code] [CRAN]
  6. Energy balancing of covariate distributions Huling, Jared D., and Mak, Simon Journal of Causal Inference (2024) [cite] [arXiv:2004.13962] [doi] [code]
  7. Robust sample weighting to facilitate individualized treatment rule learning for a target population Chen, Rui, Huling, Jared D., Chen, Guanhua, and Yu, Menggang Biometrika, 111(1):309–329 (2024) [cite] [arXiv:2105.00581] [doi]

Precision Medicine & Heterogeneity of Treatment Effect

Statistical methods for personalizing treatment decisions based on individual patient characteristics and understanding heterogeneity of treatment effect. This includes estimation of optimal individualized treatment rules, subgroup identification, and conditional average treatment effects. Includes methods for complex outcome types, multi-category treatments, and high-dimensional data.

  1. Improving RCT-Based CATE Estimation Under Covariate Mismatch via Double Calibration Pal, Samhita, Huling, Jared D., and Asiaee, Amir Preprint (2026) [cite] [arXiv:2603.17066]
  2. Estimation of heterogeneous principal effects under principal ignorability Zhang, Rui, Doss, Charles, and Huling, Jared D. Preprint (2026) [cite] [arXiv:2603.08963]
  3. An effective framework for estimating individualized treatment rules with multi-category treatments Lee, Joowon, Huling, Jared D., and Chen, Guanhua Advances in Neural Information Processing Systems (NeurIPS) (2024) [cite] [doi]
  4. Robust sample weighting to facilitate individualized treatment rule learning for a target population Chen, Rui, Huling, Jared D., Chen, Guanhua, and Yu, Menggang Biometrika, 111(1):309–329 (2024) [cite] [arXiv:2105.00581] [doi]
  5. Improving Precision of RCT-Based CATE Estimation using Data Borrowing with Double Calibration Asiaee, Amir, Di Gravio, Chiara, Beck, Cole, Mei, Yuting, Pal, Samhita, and Huling, Jared D. Preprint (2023) [cite] [arXiv:2306.17478]
  6. A reluctant additive model framework for interpretable nonlinear individualized treatment rules Maronge, Jacob M., Huling, Jared D., and Chen, Guanhua The Annals of Applied Statistics, 17(4):3384–3402 (2023) [cite] [doi]
  7. Meta-analysis of individualized treatment rules via sign-coherency Cheng, Justin J., Huling, Jared D., and Chen, Guanhua Machine Learning for Health (ML4H), PMLR, 193:171–198 (2022) [cite] [link] [code]
  8. Subgroup identification using the {personalized} package Huling, Jared D., and Yu, Menggang Journal of Statistical Software, 98(5):1–60 (2021) [cite] [doi] [code] [CRAN]
  9. Diagnosis-group-specific transitional care program recommendations for thirty-day rehospitalization reduction Yu, Menggang, Kuang, Chensheng, Huling, Jared D., and Smith, Maureen A. The Annals of Applied Statistics, 15(3):1478–1498 (2021) [cite] [doi] [code]
  10. A two-part framework for estimating individualized treatment rules from semi-continuous outcomes Huling, Jared D., Smith, Maureen A., and Chen, Guanhua Journal of the American Statistical Association, 116(533):210–223 (2021) [cite] [doi] [code] [CRAN]
  11. Fused comparative intervention scoring for heterogeneity of longitudinal intervention effects Huling, Jared D., Yu, Menggang, and Smith, Maureen A. The Annals of Applied Statistics, 13(2):824–847 (2019) [cite] [doi] [code]

Evidence Synthesis, Generalizability, Transportability, & Data-Fusion

Methods for combining evidence and data across multiple studies while preserving causal interpretability. This includes causally interpretable random-effects meta-analysis, methods combining aggregate and individual participant data from randomized trials when targeting a target population, and transportability of causal effects to target populations in the presence of patient nonadherence.

  1. Improving RCT-Based CATE Estimation Under Covariate Mismatch via Double Calibration Pal, Samhita, Huling, Jared D., and Asiaee, Amir Preprint (2026) [cite] [arXiv:2603.17066]
  2. Partially Retargeted Balancing Weights for Causal Effect Estimation Under Positivity Violations Barnard, Martha, Huling, Jared D., and Wolfson, Julian Preprint (2025)
    ★ Distinguished Student Paper Award, ENAR 2026
    [cite] [arXiv:2510.22072]
  3. Causally interpretable meta-analysis combining aggregate and individual participant data Rott, Kollin W., Clark, Justin M., Murad, M. Hassan, Hodges, James S., and Huling, Jared D. American Journal of Epidemiology, 194(7):2060–2068 (2025)
    ★ Distinguished Student Paper Award, ENAR 2024
    [cite] [doi]
  4. Transportability of Principal Causal Effects Clark, Justin M., Rott, Kollin W., Hodges, James S., and Huling, Jared D. Preprint (2024) [cite] [arXiv:2405.04419]
  5. Robust sample weighting to facilitate individualized treatment rule learning for a target population Chen, Rui, Huling, Jared D., Chen, Guanhua, and Yu, Menggang Biometrika, 111(1):309–329 (2024) [cite] [arXiv:2105.00581] [doi]
  6. Causally-Interpretable Random-Effects Meta-Analysis Clark, Justin M., Rott, Kollin W., Hodges, James S., and Huling, Jared D. Preprint (2023) [cite] [arXiv:2302.03544]
  7. Improving Precision of RCT-Based CATE Estimation using Data Borrowing with Double Calibration Asiaee, Amir, Di Gravio, Chiara, Beck, Cole, Mei, Yuting, Pal, Samhita, and Huling, Jared D. Preprint (2023) [cite] [arXiv:2306.17478]
  8. Meta-analysis of individualized treatment rules via sign-coherency Cheng, Justin J., Huling, Jared D., and Chen, Guanhua Machine Learning for Health (ML4H), PMLR, 193:171–198 (2022) [cite] [link] [code]
  9. Diagnosis-group-specific transitional care program recommendations for thirty-day rehospitalization reduction Yu, Menggang, Kuang, Chensheng, Huling, Jared D., and Smith, Maureen A. The Annals of Applied Statistics, 15(3):1478–1498 (2021) [cite] [doi] [code]

Causal Inference for Complex Treatments

Development of principled methods for confounding control and heterogeneity of effect for situations in which the treatment or intervention of scientific interest is complex and not just binary, such as continuous-valued treatments, functional treatments, and many-valued treatments.

  1. Estimating causal effects of functional treatments with modified functional treatment policies Jiang, Ziren, Cui, Erjia, and Huling, Jared D. Preprint (2026) [cite] [arXiv:2602.09145]
  2. Doss and Huling’s contribution to the Discussion of ‘Augmented balancing weights as linear regression’ by Bruns-Smith et al. Doss, Charles R., and Huling, Jared D. Journal of the Royal Statistical Society Series B: Statistical Methodology (2026) [cite] [doi]
  3. Exploring the effects of mechanical ventilator settings with modified vector-valued treatment policies Jiang, Ziren, Crooke, Philip S., Marini, John J., and Huling, Jared D. Preprint (2025) [cite] [arXiv:2507.09809]
  4. Modified treatment policy effect estimation with weighted energy distance Jiang, Ziren, and Huling, Jared D. The Annals of Applied Statistics, 19(4):2555–2577 (2025)
    ★ Distinguished Student Paper Award, ENAR 2024
    [cite] [arXiv:2310.11620] [doi]
  5. An effective framework for estimating individualized treatment rules with multi-category treatments Lee, Joowon, Huling, Jared D., and Chen, Guanhua Advances in Neural Information Processing Systems (NeurIPS) (2024) [cite] [doi]
  6. Independence weights for causal inference with continuous treatments Huling, Jared D., Greifer, Noah, and Chen, Guanhua Journal of the American Statistical Association, 119(546):1657–1670 (2024) [cite] [arXiv:2107.07086] [doi] [code] [CRAN]

Population Heterogeneity & Risk Prediction

Methods for modeling and leveraging population heterogeneity in statistical analyses and for clinical risk prediction. This work focuses on the development of statistical modeling approaches that tailor predictions and analyses in the presence of population heterogeneity instead of one-size-fits-all modeling approaches. intervention effects.

  1. Heterogeneous readmission prediction with hierarchical effect decomposition and regularization Jiang, Ziren, Huo, Lingfeng, Vaughan-Sarrazin, Mary, Smith, Maureen A., and Huling, Jared D. Preprint (2026) [cite] [arXiv:2603.19569] [code]
  2. Heterogeneity-Aware Regression with Nonparametric Estimation and Structured Selection for Hospital Readmission Prediction Wang, Wei, Bailey, Angela, Tignanelli, Christopher, and Huling, Jared D. Preprint (2025) [cite] [arXiv:2507.06388]
  3. Counterfactual fairness for small subgroups Wastvedt, Solvejg, Huling, Jared D., and Wolfson, Julian Biostatistics (2025) [cite] [arXiv:2310.19988] [doi] [code]
  4. An intersectional framework for counterfactual fairness in risk prediction Wastvedt, Solvejg, Huling, Jared D., and Wolfson, Julian Biostatistics, 25(3):702–717 (2024) [cite] [arXiv:2210.01194] [doi] [code]
  5. Doubly structured sparsity for grouped multivariate responses with application to functional outcome score modeling Huling, Jared D., Lundine, Jennifer P., and Leonard, Julie C. Statistics in Medicine, 42(15):2619–2636 (2023) [cite] [arXiv:2302.11098] [doi] [code]
  6. Sufficient dimension reduction for populations with structured heterogeneity Huling, Jared D., and Yu, Menggang Biometrics, 78(4):1626–1638 (2022) [cite] [arXiv:2212.12394] [doi] [code] [CRAN]
  7. Risk prediction for heterogeneous populations with application to hospital admission prediction Huling, Jared D., Yu, Menggang, Liang, Muxuan, and Smith, Maureen A. Biometrics, 74(2):557–565 (2018) [cite] [doi] [code] [CRAN]

High-Dimensional & Computational Methods

Variable selection and dimensionality reduction for complex, high-dimensional data, and efficient computational algorithms for large-scale statistical problems. This includes fast penalized regression, structured sparsity approaches, and optimization algorithms for large datasets.

  1. Heterogeneous readmission prediction with hierarchical effect decomposition and regularization Jiang, Ziren, Huo, Lingfeng, Vaughan-Sarrazin, Mary, Smith, Maureen A., and Huling, Jared D. Preprint (2026) [cite] [arXiv:2603.19569] [code]
  2. Data adaptive covariate balancing for causal effect estimation for high dimensional data De, Simion, and Huling, Jared D. Preprint (2025) [cite] [arXiv:2512.18069] [code]
  3. Heterogeneity-Aware Regression with Nonparametric Estimation and Structured Selection for Hospital Readmission Prediction Wang, Wei, Bailey, Angela, Tignanelli, Christopher, and Huling, Jared D. Preprint (2025) [cite] [arXiv:2507.06388]
  4. A reluctant additive model framework for interpretable nonlinear individualized treatment rules Maronge, Jacob M., Huling, Jared D., and Chen, Guanhua The Annals of Applied Statistics, 17(4):3384–3402 (2023) [cite] [doi]
  5. Doubly structured sparsity for grouped multivariate responses with application to functional outcome score modeling Huling, Jared D., Lundine, Jennifer P., and Leonard, Julie C. Statistics in Medicine, 42(15):2619–2636 (2023) [cite] [arXiv:2302.11098] [doi] [code]
  6. Sufficient dimension reduction for populations with structured heterogeneity Huling, Jared D., and Yu, Menggang Biometrics, 78(4):1626–1638 (2022) [cite] [arXiv:2212.12394] [doi] [code] [CRAN]
  7. Fast penalized regression and cross validation for tall data with the {oem} package Huling, Jared D., and Chien, Peter Journal of Statistical Software, 104(6):1–24 (2022) [cite] [arXiv:1801.09661] [doi] [code] [CRAN]
  8. Selection and estimation optimality in high dimensions with the TWIN penalty Dai, Xiaowu, and Huling, Jared D. Preprint (2021) [cite] [arXiv:1806.01936]
  9. A two-part framework for estimating individualized treatment rules from semi-continuous outcomes Huling, Jared D., Smith, Maureen A., and Chen, Guanhua Journal of the American Statistical Association, 116(533):210–223 (2021) [cite] [doi] [code] [CRAN]
  10. Risk prediction for heterogeneous populations with application to hospital admission prediction Huling, Jared D., Yu, Menggang, Liang, Muxuan, and Smith, Maureen A. Biometrics, 74(2):557–565 (2018) [cite] [doi] [code] [CRAN]
  11. Orthogonalizing EM: A design-based least squares algorithm Xiong, Shifeng, Dai, Bin, Huling, Jared D., and Qian, Peter Z. G. Technometrics, 58(3):285–293 (2016) [cite] [doi] [code] [CRAN]

Health Systems Applications

Applications of statistical methods to improve healthcare delivery and patient outcomes, including hospital risk prediction, readmission reduction, and personalized clinical intervention recommendations.

  1. Heterogeneous readmission prediction with hierarchical effect decomposition and regularization Jiang, Ziren, Huo, Lingfeng, Vaughan-Sarrazin, Mary, Smith, Maureen A., and Huling, Jared D. Preprint (2026) [cite] [arXiv:2603.19569] [code]
  2. Heterogeneity-Aware Regression with Nonparametric Estimation and Structured Selection for Hospital Readmission Prediction Wang, Wei, Bailey, Angela, Tignanelli, Christopher, and Huling, Jared D. Preprint (2025) [cite] [arXiv:2507.06388]
  3. Transportability of Principal Causal Effects Clark, Justin M., Rott, Kollin W., Hodges, James S., and Huling, Jared D. Preprint (2024) [cite] [arXiv:2405.04419]
  4. Doubly structured sparsity for grouped multivariate responses with application to functional outcome score modeling Huling, Jared D., Lundine, Jennifer P., and Leonard, Julie C. Statistics in Medicine, 42(15):2619–2636 (2023) [cite] [arXiv:2302.11098] [doi] [code]
  5. Diagnosis-group-specific transitional care program recommendations for thirty-day rehospitalization reduction Yu, Menggang, Kuang, Chensheng, Huling, Jared D., and Smith, Maureen A. The Annals of Applied Statistics, 15(3):1478–1498 (2021) [cite] [doi] [code]
  6. A two-part framework for estimating individualized treatment rules from semi-continuous outcomes Huling, Jared D., Smith, Maureen A., and Chen, Guanhua Journal of the American Statistical Association, 116(533):210–223 (2021) [cite] [doi] [code] [CRAN]
  7. Fused comparative intervention scoring for heterogeneity of longitudinal intervention effects Huling, Jared D., Yu, Menggang, and Smith, Maureen A. The Annals of Applied Statistics, 13(2):824–847 (2019) [cite] [doi] [code]
  8. Risk prediction for heterogeneous populations with application to hospital admission prediction Huling, Jared D., Yu, Menggang, Liang, Muxuan, and Smith, Maureen A. Biometrics, 74(2):557–565 (2018) [cite] [doi] [code] [CRAN]