Publications

You can also find my articles on my Google Scholar profile.

Journal Articles

Comparing traditional and causal inference methodologies for evaluating impacts of long-term air pollution exposure on hospitalization with Alzheimer disease and related dementias

Published in American Journal of Epidemiology, 2025

This study builds on previous research by applying modern statistical causal inference methodologies—generalized propensity score (GPS) weighting and matching—on a large, longitudinal dataset of 50 million Medicare enrollees to investigate impacts of air pollutants (PM2.5, NO2, and O3) on elderly patients rate of first hospitalization with an ADRD diagnosis.

Recommended citation: Qin, M. M., Khoshnevis, N., Dominici, F., Braun, D., Zanobetti, A., & Mork, D. (2025). Comparing traditional and causal inference methodologies for evaluating impacts of long-term air pollution exposure on hospitalization with Alzheimer disease and related dementias. American Journal of Epidemiology, 194(1), 64-72. https://doi.org/10.1093/aje/kwae427
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SpaCE: The Spatial Confounding Environment

Published in International Conference on Learning Representations (ICLR), 2024

We introduce SpaCE: The Spatial Confounding Environment, the first toolkit to provide realistic benchmark datasets and tools for systematically evaluating causal inference methods designed to alleviate spatial confounding. The toolkit includes training data, true counterfactuals, spatial graphs with coordinates, and realistic semi-synthetic outcomes generated using state-of-the-art machine learning ensembles.

Recommended citation: Tec, M., Trisovic, A., Audirac, M., Woodward, S., Hu, J., Khoshnevis, N., & Dominici, F. (2024). SpaCE: The Spatial Confounding Environment. In International Conference on Learning Representations (ICLR 2024).
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GPCERF-An R package for implementing Gaussian processes for estimating causal exposure response curves

Published in Journal of Open Source Software, 2024

We present the GPCERF R package, which employs a novel Bayesian approach based on Gaussian Process (GP) to estimate the causal exposure-response function (CERF) for continuous exposures, along with associated uncertainties. The package provides a two-step end-to-end solution for causal inference with continuous exposures equipped with automatic and efficient uncertainty quantification.

Recommended citation: Khoshnevis, N., Ren, B., & Braun, D. (2024). GPCERF-An R package for implementing Gaussian processes for estimating causal exposure response curves. Journal of Open Source Software, 9(95), 5465. https://doi.org/10.21105/joss.05465
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CRE: An R package for interpretable discovery and inference of heterogeneous treatment effects

Published in Journal of Open Source Software, 2023

CRE is an R package providing a flexible implementation of Causal Rule Ensemble, a new method for interpretable heterogeneous treatment effect (HTE) characterization in terms of decision rules, via an extensive exploration of heterogeneity patterns by an ensemble-of-trees approach. The package addresses limitations of single-tree heterogeneity discovery including instability and reduced exploration of potential heterogeneity.

Recommended citation: Cadei, R., Khoshnevis, N., Lee, K., Garcia, D. M., & Bargagli-Stoffi, F. J. (2023). CRE: An R package for interpretable discovery and inference of heterogeneous treatment effects. Journal of Open Source Software, 8(92), 5587. https://doi.org/10.21105/joss.05587
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Application of pool-based active learning in reducing the number of required response history analyses

Published in Computers & Structures, 2020

A step by step method is presented for reducing the need for a large number of response history analyses (RHAs) in developing surrogates to predict structural responses. We apply a pool-based query-by-committee active learning algorithm to choose the most informative data samples while preserving the accuracy of prediction models used in deriving fragility curves.

Recommended citation: Kiani, J., Camp, C., Pezeshk, S., & Khoshnevis, N. (2020). Application of pool-based active learning in reducing the number of required response history analyses. Computers & Structures, 241, 106355. https://doi.org/10.1016/j.compstruc.2020.106355
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Application of pool‐based active learning in physics‐based earthquake ground‐motion simulation

Published in Seismological Research Letters, 2019

We present the results of the application of the active learning method in developing surrogates as physics‐based earthquake ground‐motion simulators. The surrogates map input parameters into output values without demanding intensive computations, essential for parameter optimization, sensitivity, and uncertainty analysis. A step‐by‐step learning method is employed to reduce the need for generating unnecessary training data from computationally challenging numerical simulations.

Recommended citation: Khoshnevis, N., & Taborda, R. (2019). Application of pool‐based active learning in physics‐based earthquake ground‐motion simulation. Seismological Research Letters, 90(2A), 614-622. https://doi.org/10.1785/0220180144
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Prioritizing ground‐motion validation metrics using semisupervised and supervised learning

Published in Bulletin of the Seismological Society of America, 2018

We present an analysis that looks into 11 ground‐motion validation metrics using semisupervised and supervised machine learning techniques to help label and classify goodness‐of‐fit results with the objective of prioritizing and narrowing the choice of validation metrics. This addresses the subjective nature of metric selection in ground-motion simulation validation.

Recommended citation: Khoshnevis, N., & Taborda, R. (2018). Prioritizing ground‐motion validation metrics using semisupervised and supervised learning. Bulletin of the Seismological Society of America, 108(4), 2248-2264. https://doi.org/10.1785/0120180043
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Evaluation of the southern California seismic velocity models through simulation of recorded events

Published in Geophysical Journal International, 2016

This study compares seismic velocity models for southern California, focusing on CVM-S, CVM-H, and their variations. Using simulations of 30 moderate earthquakes (1998–2014) against observed data, results show that CVM-S4.26 provides the most accurate predictions of ground motion in the Los Angeles region.

Recommended citation: Ricardo Taborda, Shima Azizzadeh-Roodpish, Naeem Khoshnevis, Keli Cheng, Evaluation of the southern California seismic velocity models through simulation of recorded events, Geophysical Journal International, Volume 205, Issue 3, June 2016, Pages 1342–1364, https://doi.org/10.1093/gji/ggw085
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