Feder, Amir, Katherine A. Keith, Emaad Manzoor, Reid Pryzant, Dhanya Sridhar, Zach Wood-Doughty, Jacob Eisenstein, et al. 2022. “Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond”. Transactions of the Association for Computational Linguistics 10. Publisher’s Version: Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond.

Grimmer, Justin, Margaret E. Roberts, and Brandon M. Stewart. 2022. Text As Data: A New Framework for Machine Learning and the Social Sciences. Princeton University Press. Publisher’s Version: Text as Data: A New Framework for Machine Learning and the Social Sciences.

Grimmer, Justin, Margaret E. Roberts, and Brandon M. Stewart. 2021. “Machine Learning for Social Science: An Agnostic Approach”. Annual Review of Political Science 24. Publisher’s Version: Machine Learning for Social Science: An Agnostic Approach.

Egami, Naoki, Christian J. Fong, Justin Grimmer, Margaret E. Roberts, and Brandon M. Stewart. 2022. “How to Make Causal Inferences Using Texts”. Science Advances 8 (42).

Chuang, Jason, Margaret Roberts, Brandon Stewart, Rebecca Weiss, Dustin Tingley, Justin Grimmer, and Jeffrey Heer. 2015. “TopicCheck: Interactive Alignment for Assessing Topic Model Stability”. North American Chapter of the Association for Computational Linguistics Human Language Technologies (NAACL HLT). Denver, Colorado.

Chuang, Jason, John Wilkerson, Rebecca Weiss, Dustin Tingley, Brandon Stewart, Margaret Roberts, Forough Poursabzi-Sangdeh, et al. 2014. “Computer-Assisted Content Analysis: Topic Models for Exploring Multiple Subjective Interpretations”. Advances in Neural Information Processing Systems Workshop on Human-Propelled Machine Learning. Montreal, Canada.

Grimmer, Justin, and Brandon Stewart. 2013. “Text As Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts”. Political Analysis 21: 267-97.