Publications

See google scholar page for additional information.

• Variable importance measures for heterogeneous causal effects.
  Hines, O., Diaz-Ordaz, K., & Vansteelandt, S.
  Arxiv 2022 (Forthcoming in Biometrics).

• Automatic debiasing of neural networks via moment-constrained learning.
  Hines, C. L., & Hines, O. J.
  Proceedings of the Fourth Conference on Causal Learning and Reasoning, PMLR, 2025.

• Demystifying statistical learning based on efficient influence functions.
  Hines, O., Dukes, O., Diaz-Ordaz, K., & Vansteelandt, S.
  The American Statistician, 2022.

• Oliver Hines and Karla Diaz-Ordazʼs contribution to the discussion of ‘Assumption-lean inference for generalised linear model parameters’ by Vansteelandt and Dukes.
  Hines, O. & Diaz-Ordaz, K.
  Journal of the Royal Statistical Society Series B, 2022.

• Genetic analyses of the electrocardiographic QT interval and its components identify additional loci and pathways.
  Young, W.J., Lahrouchi, N., Isaacs, A. et al.
  Nature Communications, 2022

• Robust inference for mediated effects in partially linear models.
  Hines, O., Vansteelandt, S., & Diaz-Ordaz, K.
  Psychometrika, 2021.

• Long-term cardiovascular outcomes after orlistat therapy in patients with obesity: a nationwide, propensity-score matched cohort study.
  Ardissino, M., Vincent, M., Hines, O., Amin, R., Eichhorn, C., Tang, A. R., Collins, P., Moussa, O., Purkayastha, S.
  European Heart Journal - Cardiovascular Pharmacotherapy, 2021.

• Causal graphs for the analysis of genetic cohort data.
  Hines, O., Diaz-Ordaz, K., Vansteelandt, S., & Jamshidi, Y.
  Physiological Genomics, 2020.

Preprints

• Riesz representers for the rest of us
  Williams N. T., Hines O. J., Rudolph K. E.
  Arxiv 2025.

• Optimally weighted average derivative effects.
  Hines, O., Diaz-Ordaz, K., & Vansteelandt, S.
  Arxiv 2023.

• Parameterising the effect of a continuous exposure using average derivative effects.
  Hines, O., Diaz-Ordaz, K., & Vansteelandt, S.
  Arxiv 2021.

Presentations and workshops

• Causal inference with continuous treatments, a tale of two estimands. Symposium organised by the Centre for Microdata Methods and Practice (London, UK), December 2023.

• Bridging the divide: data science and statistics in academia and industry. Invited Ghent University Seminar (Ghent, Belgium), October 2023.

• Demystifying statistical learning based on efficient influence functions. CMStatistics (London, UK) December 2022.

• Variable importance measures for heterogeneous causal effects. International Biometric Conference (Riga, Latvia) July 2022.

• Assumption-lean causal inference for direct and indirect effects. Joint Statistical Meeting (virtual) August 2021.

• Parameterising and inferring the effect of a continuous exposure using average derivative effects. European Causal Inference Meeting (virtual), May 2021.

• Causal machine learning workshop. European Causal Inference Meeting (virtual), May 2021.

Peer review service

• American Journal of Epidemiology

• Biometrics

• Biostatistics

• International Journal of Biostatistics

• Journal of the American Statistical Association

• Journal of the Royal Statistical Society: Series B

• Scandinavian Journal of Statistics

• Statistica Sinica