Peskoff, Denis, Adam Visokay, Sander Schulhoff, Benjamin Wachspress, Alan Blinder, and Brandon M. Stewart. (2023) 2023. “GPT Deciphering Fedspeak: Quantifying Dissent Among Hawks and Doves”. Findings of the Association for Computational Linguistics: EMNLP 2023. Singapore: Association for Computational Linguistics. Referenced from aclanthology.org: GPT Deciphering Fedspeak: Quantifying Dissent Among Hawks and Doves.

Kapoor, Sayash, Emily Cantrell, Kenny Peng, Thanh Hien, Christopher A. Bai, Odd Erik, Jake M. Hofman, et al. 2023. “REFORMS: Reporting Standards for ML-Based Science”. Referenced from reforms.cs.princeton.edu: REFORMS: Reporting Standards for ML-based Science.

Rodriguez, Pedro, Arthur Spirling, Brandon M. Stewart, and Elisa M. Wirsching. 2023. “Multilanguage Word Embeddings for Social Scientists: Estimation, Inference and Validation Resources for 157 Languages”. Publisher’s Version: Multilanguage Word Embeddings for Social Scientists: Estimation, Inference and Validation Resources for 157 Languages.

Egami, Naoki, Musashi Hinck, Brandon M. Stewart, and Hanying Wei. 2023. “Using Imperfect Surrogates for Downstream Inference: Design-Based Supervised Learning for Social Science Applications of Large Language Models”. Proceedings of Advances in Neural Information Processing Systems.

Felton, Chris, and Brandon M. Stewart. n.d. “Handle With Care: A Sociologist’s Guide to Causal Inference With Instrumental Variables.”

Spirling, Arthur, and Brandon M. Stewart. n.d. “What Good Is a Regression? Inference to the Best Explanation and the Practice of Political Science Research.”

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.

Rodriguez, Pedro, Arthur Spirling, and Brandon M. Stewart. 2023. “Embedding Regression: Models for Context-Specific Description and Inference”. American Political Science Review 117 (4): 1255-74. doi:10.1017/S0003055422001228.

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.

Mathur, Arunesh, Angelina Wang, Carsten Schwemmer, Maia Hamin, Brandon M. Stewart, and Arvind Narayanan. (2023) 2023. “Manipulative Tactics Are the Norm in Political Emails: Evidence from 300K Emails from the 2020 U.S. Election Cycle”. Big Data & Society 10 (1). doi:10.1177/20539517221145371.

Grimmer, Justin, Dean Knox, and Brandon M. Stewart. (2023) 2023. “Naïve Regression Requires Weaker Assumptions Than Factor Models to Adjust for Multiple Cause Confounding”. Journal of Machine Learning Research 24 (182): 1-70. Publisher’s Version: Naïve regression requires weaker assumptions than factor models to adjust for multiple cause confounding.

Ying, Luwei, Jacob Montgomery, and Brandon M. Stewart. 2022. “Topics, Concepts, and Measurement: A Crowdsourced Procedure for Validating Topics As Measures”. Political Analysis 30 (4). Publisher’s Version: Topics, Concepts, and Measurement: A Crowdsourced Procedure for Validating Topics as Measures.

Lundberg, Ian, and Brandon M. Stewart. 2020. “Comment: Summarizing Income Mobility With Multiple Smooth Quantiles Instead of Parameterized Means”. Sociological Methodology 50: 96-111. Publisher’s Version: Comment: Summarizing income mobility with multiple smooth quantiles instead of parameterized means.

Marchi’, ’Scott, and Brandon M. Stewart. 2020. “Computational and Machine Learning Models: The Necessity of Connecting Theory and Empirics”. In SAGE Handbook of Research Methods in Political Science and International Relations. SAGE.

Lundberg, Ian, Rebecca Johnson, and Brandon M. Stewart. 2021. “What Is Your Estimand? Defining the Target Quantity Connects Statistical Evidence to Theory”. American Sociological Review 86 (3): 532-65.

Horowitz, Michael, Brandon M. Stewart, Dustin Tingley, Michael Bishop, Laura Resnick Samotin, Margaret Roberts, Welton Chang, Barbara Mellers, and Philip Tetlock. 2019. “What Makes Foreign Policy Teams Tick: Explaining Variation in Group Performance at Geopolitical Forecasting”. The Journal of Politics 81 (4): 1388-1404. doi:10.1086/704437.

Yeomans, Michael, Brandon M. Stewart, Kimia Mavon, Alex Kindel, Dustin Tingley, and Justin Reich. 2018. “The Civic Mission of MOOCs: Computational Measures of Engagement Across Differences in Online Courses”. International Journal of Artificial Intelligence in Education 28 (4): 553-89. Publisher’s Version: The Civic Mission of MOOCs: Computational Measures of Engagement Across Differences in Online Courses.

Chaney, Allison J.B., Brandon M. Stewart, and Barbara E. Engelhardt. 2018. “How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility”. Twelfth ACM Conference on Recommender Systems (RecSys ’18). Vancouver, BC, Canada.: ACM. arXiv: How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility.

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).

Roberts, Margaret E., Brandon M. Stewart, and Richard Nielsen. 2020. “Adjusting for Confounding With Text Matching”. American Journal of Political Science 64 (4): 887-903. Publisher’s Version: Adjusting for Confounding with Text Matching.

Stewart, Brandon M. 2014. “Latent Factor Regressions for the Social Sciences.”

Roberts, Margaret E., Brandon M. Stewart, and Edoardo Airoldi. 2016. “A Model of Text for Experimentation in the Social Sciences”. Journal of the American Statistical Association 111 (515): 988-1003. Publisher’s Version: A model of text for experimentation in the social sciences.

Simmons, Beth A., Paulette Lloyd, and Brandon M. Stewart. 2018. “The Global Diffusion of Law: Transnational Crime and the Case of Human Trafficking”. International Organization 72 (2): 249-81. Publisher’s Version: The Global Diffusion of Law: Transnational Crime and the Case of Human Trafficking.

Lucas, Christopher, Richard Nielsen, Margaret E. Roberts, Brandon M. Stewart, Alex Storer, and Dustin Tingley. 2015. “Computer Assisted Text Analysis for Comparative Politics.”. Political Analysis 23 (2): 254-77.

Zhukov, Yuri M., and Brandon M. Stewart. 2013. “Choosing Your Neighbors: Networks of Diffusion in International Relations”. International Studies Quarterly 57: 271-87.

Stewart, Brandon M., and Yuri M. Zhukov. 2009. “Use of Force and civil–military Relations in Russia: An Automated Content Analysis”. Small Wars & Insurgencies 20: 319-43.