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) machine learning (e.g., Ertefaie et al., 2023); corrections for semi-parametric estimation of treatment effects with auxiliary- or outcome-dependent, two-phase sampling designs (Hejazi et al., 2021a); and application of flexible intervention schemes in settings with continuous exposures and in which 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.

Boileau, P., Hejazi, N.S. and Dudoit, S. (2020) Exploring high-dimensional biological data with sparse contrastive principal component analysis. Bioinformatics, 36, 3422–3430.

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.

Ertefaie, A., Hejazi, N.S. and van der Laan, M.J. (2023) Nonparametric inverse-probability-weighted estimators based on the highly adaptive lasso. Biometrics, 79, 1029–1041.

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, 8^th (annual).