• Rubinstein, M., Cuellar, M., Malinsky, D. (2025) Mediated probabilities of causation, Journal of Causal Inference (forthcoming).
• Cuellar, M., Vanderplas, S., Luby, A., Rosenblum, M., (2024). Methodological problems in every black-box study of forensic firearm comparisons, Law, Probability and Risk, 23(1), mgae015.
  • Note: This article has been filed in at least one case in CO, DC, IL, IA, MD, NY, OR, and Federal Court.
• Cuellar, M., Gao, S. and Hofmann, H., (2024). An algorithm for forensic toolmark comparisons. Forensic Science International: Synergy, 9, p.100543.
• Rosenblum, M., Chin, E.T., Ogburn, E.L., Nishimura, A., Westreich, D., Datta, A., Vanderplas, S., Cuellar, M. and Thompson, W.C., (2024). Incorrect statistical reasoning in Guyll et al. leads to biased claims about strength of forensic evidence. Procedures of the National Academies of Science, 121(45) e2315431121.
• Cuellar, M., Mauro, J., Luby, A. (2022); A probabilistic formulation of contextual bias in forensic analysis, Journal of the Royal Statistical Society, Series A. 185 (2) (S620–S643).
• Cuellar, M., Gonzalez, C., Dror, I. (2022). Human and machine similarity judgments in forensic firearm comparisons. Forensic Science International: Synergy, (5) 100283.
• Chalfin, A., Kaplan, J., Cuellar, M. (2021). Measuring marginal crime concentration: A new solution to an old problem. Journal of Research in Crime and Delinquency, 58(4),467–504.
• Cuellar, M., & Kennedy, E. H. (2020). A non-parametric projection-based estimator for the probability of causation, with application to water sanitation in Kenya. Journal of the Royal Statistical Society: Series A, 183(4), 1793-1818.
• Mejia, R., Cuellar, M., Salyards, J. (2020). Implementing Blind Proficiency Testing in Forensic Laboratories: Motivation, Obstacles, and Recommendations. Forensic Science International: Synergy, (2), 293-298.
• Ling, S., Kaplan, J., Cuellar, M. (2020). Public Beliefs About the Accuracy and Importance of Forensic Evidence in the United States. Science & Justice, 60(3), 263-272.
• Moral, J., Hundl, C., Lee, D., Neuman, M., Grimaldi, A., Cuellar, M., Stout, P. (2019). Implementation of a blind quality control program in blood alcohol analysis. Journal of Analytical Toxicology, 43(8), 1-7.
• Cuellar, M. (2018). Trends in Self-Reporting of Marijuana Consumption in the United States. Statistics and Public Policy, 5(1), 1-10.
• Cuellar, M. (2017). Causal reasoning and data analysis: Problems with the abusive head trauma diagnosis, Law, Probability and Risk; 16(4): 223-239.

Chapters

• Cuellar, M. (2022). Causes of Effects and Effects of Causes. In Statistics in the Public Interest, Data Sciences Book Series, (pp. 211-233). Springer, Cham.
• Cuellar, M., Mentch, L., Spiegelman, C. (2019). Association does not imply discrimination: Flawed analyses that lead to wrongful convictions. In Handbook of Forensic Statistics, (pp. 131–142). Chapman & Hall/CRC Handbooks of Modern Statistical Methods.

Works in progress

• The Overlooked Risks of Non-Validated Exclusions doi.org/10.48550/arXiv.2412.05398 (revise and resubmit)
• How Often are Fingerprints Repeated in the Population? Expanding on Evidence from AI With the Birthday Paradox – with Jackson Gold doi.org/10.48550/arXiv.2412.13135 (under review)
• The Statistical Foundations and Diagnostic Challenges of Shaken Baby Syndrome/Abusive Head Trauma (SBS/AHT) doi.org/10.48550/arXiv.2412.10648 (under review)
• The Prosecutor’s fallacy and the likelihood ratio. doi.org/10.48550/arXiv.2502.03217 (under review)
• Accuracy and fairness in police use of facial recognition – with James To and Arush Mehrotra.
• A dataset of 3D toolmarks for applications in forensic statistics – with Heike Hofmann.
• A hierarchical model to study forensic toolmark identification – with Shane Jensen, Alex Knorre.
• The causal effect of Covid-19 lockdowns on crime in Bogotá, Colombia – with Javier Rojas Aguilera, Angela Zorro Medina.
• Review of fingerprint black-box studies – Michael Rosenblum, Betsy Ogburn, Liz Chin, Amanda Luby, others.