Biography

Daniel Ho's scholarship centers on quantitative empirical legal studies, with a substantive focus on administrative, antidiscrimination, and election law. He has written on the impact of war on Supreme Court civil rights and liberties decisions, the effect of affirmative action, and the consequences of local electoral administration on voting behavior. Prior to joining Stanford Law School, he clerked for Judge Stephen F. Williams on the U.S. Court of Appeals, District of Columbia Circuit and was a post-doctoral fellow at the Institute for Quantitative Social Science at Harvard University. He was co-recipient of McGraw-Hill Award for the best paper published by political scientists on law and courts (2006) and the Pi Sigma Alpha award for the best paper delivered at the Midwest Political Science Association (2004).

Key Works

• Daniel E. Ho and Kevin M. Quinn, Viewpoint Diversity and Media Consolidation: An Empirical Study, 61 Stanford Law Review (forthcoming 2008).
• Daniel E. Ho, Kosuke Imai, Gary King and Elizabeth A. Stuart, Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference, 15 Political Analysis 199 (2007).
• Daniel E. Ho, Affirmative Action's Affirmative Actions: a Reply to Sander, 114 Yale Law Journal 2011 (2005).
• Daniel E. Ho and Kosuke Imai, Randomization Inference with Natural Experiments: an Analysis of Ballot Effects in the 2003 California Recall Election, 101 Journal of the American Statistical Association 888 (2006).
• Lee Epstein, Daniel E. Ho, Gary King and Jeffrey A. Segal, The Supreme Court During Crisis: How War Affects Only Non-war Cases, 80 New York University Law Review 1 (2005).

Courses & Programs

Courses

• Administrative Law
• Statistical Inference in Law

Publications & Cases

Recent Publications View All

• Daniel E. Ho and Kevin M. Quinn, Improving the Presentation and Interpretation of Online Ratings Data with Model-based Figures, American Statistician (forthcoming 2008).
• Daniel E. Ho and Timothy H. Shapiro, Evaluating Course Evaluations: An Empirical Analysis of a Quasi-Experiment at Stanford Law School, 2000-2007, Journal of Legal Education (forthcoming 2008).
• Daniel E. Ho and Kevin M. Quinn, Viewpoint Diversity and Media Consolidation: An Empirical Study, 61 Stanford Law Review (forthcoming 2008).
• Daniel E. Ho, Kosuke Imai, Gary King and Elizabeth A. Stuart, MatchIt: Nonparametric Preprocessing for Parametric Causal Inference, Journal of Statistical Software (forthcoming 2008).
• Daniel E. Ho and Kosuke Imai, Estimating Causal Effects of Ballot Order from a Randomized Experiment: The California Alphabet Lottery, 1978-2002, Public Opinion Quarterly (forthcoming 2008).
• Daniel E. Ho, Kosuke Imai, Gary King and Elizabeth A. Stuart, Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference, 15 Political Analysis 199 (2007).
• John J. Donohue III and Daniel E. Ho, The Impact of Damage Caps on Malpractice Claims: Randomization Inference with Difference-in-Differences, 4 Journal of Empirical Legal Studies 69 (2007).
• Daniel E. Ho and Kevin M. Quinn, Assessing Political Positions of Media, Stanford Law and Economics Olin Working Paper, No. 343 (2007).
• Daniel E. Ho, Affirmative Action's Affirmative Actions: a Reply to Sander, 114 Yale Law Journal 2011 (2005).
• Daniel E. Ho and Kosuke Imai, Randomization Inference with Natural Experiments: an Analysis of Ballot Effects in the 2003 California Recall Election, 101 Journal of the American Statistical Association 888 (2006).