Daniel L. Chen, Vardges Levonyan, S.Eric Reinhart, and Glen Taksler, “Mandatory Disclosure: Theory and Evidence from Industry-Physician Relationships”, Journal of Legal Studies, vol. 48, n. 2, June 2019, pp. 409–440.

Daniel L. Chen, “Law and Literature: Theory and Evidence on Empathy and Guile”, Review of Law and Economics, vol. 15, n. 1, March 2019.

Daniel L. Chen, “Judicial Analytics and the Great Transformation of American Law”, Artificial Intelligence and the Law, vol. 27, n. 1, March 2019, pp. 15–42.

Daniel L. Chen, Yosh Halberstam, Manoj Kumar, and Alan Yu, “Attorney Voice and the U.S. Supreme Court”, 2019in Law as Data: Computation, Text, and the Future of Legal Analysis, Michael Livermore, and Daniel Rockmore (eds.), Santa Fe Institute Press, 2019.

Daniel L. Chen, Charlotte Cavaillé, and Karine Van Der Straeten, “A Decision-Theoretic Approach to Understanding Survey Response: Likert vs. Quadratic Voting for Attitudinal Research”, The University of Chicago Law Review, vol. 22, n. Online, 2019.

Daniel L. Chen, “Intermediated Social Preferences: Altruism in an Algorithmic Era”, in Advances in Economics of Religion, Jean-Paul Carvalho, Sriya Iyer, and Jared Rubin (eds.), Palgrave Macmillan, series “International Economic Association Series”, vol. 158, 2019, pp. 119–138.

Daniel L. Chen, and Elliott Ash, “Case Vectors: Spatial Representations of the Law Using Document Embeddings”, Law as Data, vol. 11, 2019.

Daniel L. Chen, “Machine Learning and the Rule of Law”, 2019in Law as Data: Computation, Text, and the Future of Legal Analysis, Michael Livermore, and Daniel Rockmore (eds.), Santa Fe Institute Press, 2019.

Daniel L. Chen, Jacob Phillips, and Alan Yu, “Non-Segmental Conditioning of Sibilant Variation in American English”, in Proceedings of the 9th International Conference on Speech Prosody, 2018.

Ramya Vunikili, Hitesh Ochani, Divisha Jaiswal, Richa Deshmukh, Daniel L. Chen, and Elliott Ash, “Analysis of Vocal Implicit Bias in SCOTUS Decisions Through Predictive Modelling”, in Proceedings of Experimental Linguistics, 2018.

Daniel L. Chen, Matt Dunn, Levent Sagun, and Hale Sirin, “Early Predictability of Asylum Court Decisions”, in Proceedings of the ACM Conference on AI and the Law, 2018.

Daniel L. Chen, and Jess Eagel, “Can Machine Learning Help Predict the Outcome of Asylum Adjudications?”, in Proceedings of the ACM Conference on AI and the Law, 2018.

Elliott Ash, and Daniel L. Chen, “What Kind of Judge is Brett Kavanaugh?: A Quantitative Analysis”, Cardozo Law Review, 2018, pp. 70–100.

Daniel L. Chen, “Tastes for Desert and Placation: A Reference Point-Dependent Model of Social Preferences”, Research in Experimental Economics, 2018.

Adam B. Badawi, and Daniel L. Chen, “The Shareholder Wealth Effects of Delaware Litigation”, American Law and Economics Review, vol. 19, n. 2, October 2017, pp. 287–326.

Carlos Berdejo, and Daniel L. Chen, “Electoral Cycles Among U.S. Courts of Appeals Judges”, The Journal of Law and Economics, vol. 60, n. 3, August 2017, pp. 479–496.

Shivam Verma, Adithya Parthasarathy, and Daniel L. Chen, “The Genealogy of Ideology: Identifying Persuasive Memes and Predicting Agreement in the U.S. Courts of Appeals”, in Proceedings of the ACM Conference on AI and the Law, June 2017, pp. 253 – 256.

Daniel L. Chen, Tobias J. Moskowitz, and Kelly Shue, “Decision-Making under the Gambler's Fallacy: Evidence from Asylum Judges, Loan Officers, and Baseball Umpires”, The Quarterly Journal of Economics, vol. 131, n. 3, August 2016, pp. 1181–1241.

Daniel L. Chen, and John J. Horton, “Are Online Labor Markets Spot Markets for Tasks? A Field Experiment on the Behavioral Response to Wages Cuts”, Information Systems Research, vol. 27, n. 2, May 2016, pp. 403–423.

Daniel L. Chen, Yosh Halberstam, and Alan Yu, “Perceived Masculinity Predicts U.S. Supreme Court Outcomes”, Plos One, vol. 11, n. e0164324, 2016.