Peer Reviewed Publications

Valdenegro, D., Yan, J., Dai, D., & Rahal, C. (2026). RobustiPy: An efficient next-generation multiversal library with model selection, averaging, resampling, and explainable AI. Patterns, 7(8), 101609.

Zhang, L., Rahal, C., Kanopka, K., Ulitzsch, E., Zhang, Z., & Domingue, B. W. (2026). Evaluating model predictive performance in confirmatory factor analysis with binary outcomes using the InterModel Vigorish. Multivariate Behavioral Research, 1-20.

Domingue, B. W., Rahal, C., Faul, J., Freese, J., Kanopka, K., Rigos, A., Stenhaug, B., & Tripathi, A. S. (2025). The InterModel Vigorish (IMV) as a flexible and portable approach for quantifying predictive accuracy with binary outcomes. PLOS ONE, 20(3), e0316491.

Yan, J., & Rahal, C. (2025). On the unknowable limits to prediction. Nature Computational Science, 5(3), 188-190.

Muggleton, N., Rahal, C., & Reeves, A. (2025). Capitalizing on a crisis: A computational analysis of all five million British firms during the Covid-19 pandemic. Journal of Computational Social Science, 8(2), 1-29.

Rahal, C., & Mohan, J. (2024). The role of the third sector in public health service provision: Evidence from 25,338 heterogeneous procurement datasets. Journal of the Royal Statistical Society Series A: Statistics in Society, qnae092.

Domingue, B. W., Kanopka, K., Kapoor, R., Pohl, S., Chalmers, R. P., Rahal, C., & Rhemtulla, M. (2024). The InterModel Vigorish as a lens for understanding (and quantifying) the value of item response models for dichotomously coded items. Psychometrika, 1-21.

Newman, S. J., Schulte, K., Morellini, M. M., Rahal, C., & Leasure, D. R. (2024). Offshoring emissions through used vehicle exports. Nature Climate Change, 14(3), 238-241. https://doi.org/10.1038/s41558-024-01943-1

Rahal, C., Verhagen, M., & Kirk, D. (2021). The rise of machine learning in the academic social sciences. AI & Society. https://doi.org/10.1007/s00146-022-01540-w

Mills, M. C., & Rahal, C. (2021). Population Studies at 75 years: An empirical review. Population Studies, 75, 7-25. https://doi.org/10.1080/00324728.2021.1996624

Aburto, J. M., Schöley, J., Kashnitsky, I., Zhang, L., Rahal, C., Missov, T., Mills, M. C., Dowd, J. B., & Kashyap, R. (2021). Quantifying impacts of the COVID-19 pandemic through life-expectancy losses: A population-level study of 29 countries. International Journal of Epidemiology, 51(1), 63-74.

Block, P., Hoffman, M., Raabe, I., Dowd, J., Rahal, C., Kashyap, R., & Mills, M. C. (2020). Social network-based distancing strategies to flatten the COVID-19 curve in a post-lockdown world. Nature Human Behaviour.

Mills, M. C., & Rahal, C. (2020). The GWAS Diversity Monitor tracks diversity by disease in real time. Nature Genetics, 52.

Rahal, C. (2019). Tools for transparency in central government spending. International Journal of Population Data Science, 4(1), 1092.

Mills, M. C., & Rahal, C. (2019). A scientometric review of genome-wide association studies. Communications Biology, 2(9).

Rahal, C. (2018). The keys to unlocking public payments data. Kyklos, 71(2).

Reeves, A., Friedman, S., Rahal, C., & Flemmen, M. (2017). The decline and persistence of the old boy: Private schools and elite recruitment 1897 to 2016. American Sociological Review, 82(6).

Tropf, F., Lee, S., Verweij, R., Stulp, G., van der Most, P., de Vlaming, R., Bakshi, A., et al. (2017). Hidden heritability due to heterogeneity across seven populations. Nature Human Behaviour, 1(10).

Rahal, C. (2016). Housing markets and unconventional monetary policy. Journal of Housing Economics, 32, 67-80.

Rahal, C. (2015). A guide to the StatFact EViews add-in. Computational Economics, 48(1), 183-188.