Methodological research has largely focused on the development of techniques for causal inference, with specific areas of focus including causal mediation analysis with complex exposures and subject to intermediate confounding (e.g., Dı́az et al., 2020, 2021; Hejazi et al., 2023b); causal (or de-biased or targeted) machine learning (e.g., Ertefaie et al., 2023; Hejazi et al., 2025); corrections necessary for semi-parametric estimation of treatment effects when auxiliary- or outcome-dependent, two-phase sampling designs are used to measure outcomes or putative mediators, respectively (Hejazi et al., 2021a); and application of flexible intervention schemes in settings in which continuous exposures are present and non-interference between units breaks down (Balkus et al., 2025).
Other areas of focus have varied fairly widely, including procedures that use causal inference and semi-parametric estimation to identify biomarkers and assess effect modification based on high-dimensional biological data (Hejazi et al., 2023a; Boileau et al., 2025); causal survival analysis with competing risks (Dı́az et al., 2024); asymptotically optimal loss-based selection of covariance matrix estimators in high-dimensional settings (Boileau et al., 2023); and causal segment discovery in large-scale randomized digital experiments (Hejazi et al., 2021b).
selected publications
Some representative publications appear below. Please consult Google Scholar or a recent CV for a comprehensive list. Below, the names of lab members appear in bold, including those of trainees and of the PI; the names of frequent collaborators are italicized and underlined.
Balkus, S.V., Delaney, S. and
Hejazi, N.S. (2025)
The causal effects of modified treatment policies under network interference.
revision invited at Journal of the Royal Statistical Society, Series B: Statistical Methodology.
Barnatchez, K., Josey, K.P.,
Hejazi, N.S., Shepherd, B.E., Parmigiani, G. and Nethery, R.C. (2025)
Efficient estimation of causal effects under two-phase sampling with error-prone outcome and treatment measurements.
under review.
Boileau, P.,
Hejazi, N.S.,
van der Laan, M.J. and Dudoit, S. (2023)
Cross-validated loss-based covariance matrix estimator selection in high dimensions.
Journal of Computational and Graphical Statistics,
32, 601–612.
Boileau, P., Leng, N.,
Hejazi, N.S.,
van der Laan, M.J. and Dudoit, S. (2025)
A nonparametric framework for treatment effect modifier discovery in high dimensions.
Journal of the Royal Statistical Society, Series B: Statistical Methodology,
87, 157–185.
Dı́az, I. and
Hejazi, N.S. (2020)
Causal mediation analysis for stochastic interventions.
Journal of the Royal Statistical Society, Series B: Statistical Methodology,
82, 661–683.
Dı́az, I.,
Hejazi, N.S.,
Rudolph, K.E. and
van der Laan, M.J. (2021)
Non-parametric efficient causal mediation with intermediate confounders.
Biometrika,
108, 627–641.
Dı́az, I., Williams, N.T., Hoffman, K.L. and
Hejazi, N.S. (2024)
Causal survival analysis under competing risks using longitudinal modified treatment policies.
Lifetime Data Analysis,
30, 213–236.
Hejazi, N.S.,
Boileau, P.,
van der Laan, M.J. and
Hubbard, A.E. (2023a)
A generalization of moderated statistics to data adaptive semiparametric estimation in high-dimensional biology.
Statistical Methods in Medical Research,
32, 539–554.
Hejazi, N.S.,
Rudolph, K.E.,
van der Laan, M.J. and
Dı́az, I. (2023b)
Nonparametric causal mediation analysis for stochastic interventional (in)direct effects.
Biostatistics,
24, 686–707.
Hejazi, N.S.,
van der Laan, M.J., Janes, H.E.,
Gilbert, P.B. and Benkeser, D.C. (2021a)
Efficient nonparametric inference on the effects of stochastic interventions under two-phase sampling, with applications to vaccine efficacy trials.
Biometrics,
77, 1241–1253.
Hejazi, N.S., Zheng, W. and Anand, S. (2021b)
A framework for causal segmentation analysis with machine learning in large-scale digital experiments.
Conference on Digital Experimentation at MIT,
8th (annual).