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>, https://doi.org/10.1111/rssa.12788,, https://ideas.repec.org/a/bla/jorssa/v185y2022i2p566-587.html.

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>, https://doi.org/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 (2022). “A framework for credible, prediction of state firearm legislative activity with dynamic network, models.”

Clark DA, Handcock MS (2024). “Causal inference over stochastic, networks.” Journal of the Royal Statistical Society Series A:, Statistics in Society, qnae001. ISSN 0964-1998,, <doi:10.1093/jrsssa/qnae001> https://doi.org/10.1093/jrsssa/qnae001,, https://academic.oup.com/jrsssa/advance-article-pdf/doi/10.1093/jrsssa/qnae001/56420431/qnae001.pdf,, https://doi.org/10.1093/jrsssa/qnae001.

Working Papers

Clark DA, Handcock MS (2022). “Bayesian Inference for Latent Order, Logistic Network Models.”