Oliver Hines - research

Selected Publications

See google scholar for a full list.

Parameterising the effect of a continuous treatment using average derivative effects.
Hines, O. J., Diaz-Ordaz, K., & Vansteelandt, S.
Biometrika, 2026.
Variable importance measures for heterogeneous causal effects.
Hines, O. J., Diaz-Ordaz, K., & Vansteelandt, S.
Biometrics, 2025.
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.
Robust inference for mediated effects in partially linear models.
Hines, O., Vansteelandt, S., & Diaz-Ordaz, K.
Psychometrika, 2021.
Causal graphs for the analysis of genetic cohort data.
Hines, O., Diaz-Ordaz, K., Vansteelandt, S., & Jamshidi, Y.
Physiological Genomics, 2020.

Variable importance measures for heterogeneous treatment effects. Joint Statistical Meeting (Nashville, USA), August 2025
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.