Many problems in the social sciences are inherently relational, that is observations are inextricably linked to other observations. This does not lend itself to many traditional statistical approaches and generates many ongoing, difficult and interesting problems. The majority of my work has been in developing methods for social network analysis, with a particular interest in applications for approximate causal inference in a network setting.
Publications
Clark DA, Handcock MS (2022). “Comparing the real-world performance of exponential-family random graph models and latent order logistic models for social network analysis.” Journal of the Royal Statistical Society Series A, 185(2), 566-587. doi:10.1111/rssa.12788
Clark DA, Macinko J, Porfiri M (2022). “What factors drive state firearm law adoption? An application of exponential-family random graph models.” Social Science & Medicine, 305, 115103. doi:10.1016/j.socscimed.2022.115103
Macinko J, Pomeranz JL, Clark DA, Porfiri M (2023). “The diffusion of punitive firearm preemption laws across US states.” American Journal of Preventive Medicine.
Clark DA, Macinko J, Porfiri M (2024). “Modeling State Firearm Law Adoption Using Temporal Network Models.” The Milbank Quarterly, 102(1), 97-121. doi:10.1111/1468-0009.12677
Clark DA, Handcock MS (2024). “Causal inference over stochastic networks.” Journal of the Royal Statistical Society Series A: Statistics in Society, qnae001. doi:10.1093/jrsssa/qnae001
Preprints
Clark DA, Kresin CJ, Jones-Todd CM (2026). “A General Marked Point Process Framework For Self-Exciting Network Evolution.” arXiv:2505.22659
Kresin CJ, Clark DA, Davis L, Hazelton M (2026). “Causal inference for spatiotemporal point processes in the presence of outcome spillover and carryover.” arXiv:2604.12124
Clark DA (2026). “A Sensitivity Framework for Identifying Contagion under Latent Homophily for Fixed-in-Time Network Analyses, with an Application to U.S. House Congressional Voting.” arXiv:2606.18197
Working Papers
Clark DA, Handcock MS (2022). “Bayesian Inference for Latent Order Logistic Network Models.”
Fellows IE, Clark DA, Handcock MS (2025). “ernm: An R package for Exponential-family Random Network Models.” Submitted to Journal of Statistical Software.