Daniel L. Chen, « Judicial Analytics and the Great Transformation of American Law », Artificial Intelligence and the Law, vol. 27, n° 1, mars 2019, p. 15–42.
Daniel L. Chen, « Machine Learning and Rule of Law », Computational Analysis of Law, vol. 27, n° 1, mars 2019, p. 15–42.
Daniel L. Chen, « Intermediated Social Preferences: Altruism in an Algorithmic Era », dans Advances in Economics of Religion, sous la direction de Jean-Paul Carvalho, Sriya Iyer et Jared Rubin, Palgrave Macmillan, collection « International Economic Association Series », vol. 158, 2019, p. 119–138.
Daniel L. Chen et Elliott Ash, « Case Vectors: Spatial Representations of the Law Using Document Embeddings », Law as Data, vol. 11, 2019.
Daniel L. Chen, Yosh Halberstam, Manoj Kumar et Alan Yu, « Attorney Voice and the U.S. Supreme Court », 2019dans Law as Data, sous la direction de Michael Livermore et Daniel Rockmore, Santa Fe Institute Press, 2019.
Elliott Ash et Daniel L. Chen, « What Kind of Judge is Brett Kavanaugh?: A Quantitative Analysis », Cardozo Law Review, 2018, p. 70–100.
Adam B. Badawi et Daniel L. Chen, « The Shareholder Wealth Effects of Delaware Litigation », American Law and Economics Review, vol. 19, n° 2, octobre 2017, p. 287–326.
Carlos Berdejo et Daniel L. Chen, « Electoral Cycles Among U.S. Courts of Appeals Judges », The Journal of Law and Economics, vol. 60, n° 3, août 2017, p. 479–496.
Daniel L. Chen, Tobias J. Moskowitz et 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, août 2016, p. 1181–1241.
Daniel L. Chen et 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, mai 2016, p. 403–423.
Daniel L. Chen, Yosh Halberstam et Alan Yu, « Perceived Masculinity Predicts U.S. Supreme Court Outcomes », Plos One, vol. 11, n° e0164324, 2016.
Daniel L. Chen, Martin Schonger et Chris Wickens, « oTree - An open-source platform for laboratory, online, and field experiments », Journal of Behavioral and Experimental Finance, vol. 9, n° 1, 2016, p. 88–97.
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