Daniel E. Ho is the William Benjamin Scott and Luna M. Scott Professor of Law at Stanford Law School, Professor of Political Science, Senior Fellow at the Stanford Institute for Economic Policy Research, Associate Director of the Stanford Institute for Human-Centered Artificial Intelligence, and Director of the Regulation, Evaluation, and Governance Lab (RegLab).

He is also a Faculty Fellow at the Center for Advanced Study in the Behavioral Sciences, Faculty Affiliate at the Woods Institute for the Environment, and Faculty Affiliate at the Wilson Sheehan Lab for Economic Opportunities.

His scholarship centers on quantitative empirical legal studies, with a substantive focus on administrative law and regulatory policy, antidiscrimination law, and courts. He received his J.D. from Yale Law School and his Ph.D. in political science from Harvard University, and clerked for Judge Stephen F. Williams on the U.S. Court of Appeals, District of Columbia Circuit. His research has appeared in journals such as the Stanford Law Review, the Yale Law Journal, the N.Y.U. Law Review, the Journal of the American Statistical Association, the American Statistician, the Journal of Law, Economics, and Organization, the American Economic Journal: Economic Policy, Quarterly Journal of Political Science, Political Analysis, the Journal of Legal Studies, and the Journal of Empirical Legal Studies. He is the recipient of numerous awards, including the John Bingham Hurlbut Award for Excellence in Teaching at Stanford Law School (2010), the Warren Miller prize for the best paper published in Political Analysis (2008), and the Pi Sigma Alpha award for the best paper delivered at the Midwest Political Science Association meeting (2004). He served as President for the Society of Empirical Legal Studies (2011-12) and as co-editor of the Journal of Law, Economics, and Organization (2013-16).

Administrative Appointments

  • Director, Regulation, Evaluation, and Governance Lab (2018 - Present)
  • Associate Director, Stanford Institute for Human-Centered Artificial Intelligence (2019 - Present)

Program Affiliations

  • Public Policy
  • Symbolic Systems Program

Stanford Advisees

All Publications

  • Implications of predicting race variables from medical images. Science (New York, N.Y.) Zou, J., Gichoya, J. W., Ho, D. E., Obermeyer, Z. 2023; 381 (6654): 149-150


    AI-predicted race variables pose risks and opportunities for studying health disparities.

    View details for DOI 10.1126/science.adh4260

    View details for PubMedID 37440627

  • Integrating social services with disease investigation: A randomized trial of COVID-19 high-touch contact tracing. PloS one Lu, L. C., Ouyang, D., D'Agostino, A., Diaz, A., Rudman, S. L., Ho, D. E. 2023; 18 (5): e0285752


    COVID-19 exposed and exacerbated health disparities, and a core challenge has been how to adapt pandemic response and public health in light of these disproportionate health burdens. Responding to this challenge, the County of Santa Clara Public Health Department designed a model of "high-touch" contact tracing that integrated social services with disease investigation, providing continued support and resource linkage for clients from structurally vulnerable communities. We report results from a cluster randomized trial of 5,430 cases from February to May 2021 to assess the ability of high-touch contact tracing to aid with isolation and quarantine. Using individual-level data on resource referral and uptake outcomes, we find that the intervention, randomized assignment to the high-touch program, increased the referral rate to social services by 8.4% (95% confidence interval, 0.8%-15.9%) and the uptake rate by 4.9% (-0.2%-10.0%), with the most pronounced increases in referrals and uptake of food assistance. These findings demonstrate that social services can be effectively combined with contact tracing to better promote health equity, demonstrating a novel path for the future of public health.

    View details for DOI 10.1371/journal.pone.0285752

    View details for PubMedID 37192191

    View details for PubMedCentralID PMC10187910

  • Automated vs. manual case investigation and contact tracing for pandemic surveillance: Evidence from a stepped wedge cluster randomized trial. EClinicalMedicine Raymond, C., Ouyang, D., D'Agostino, A., Rudman, S. L., Ho, D. E. 2023; 55: 101726


    Background: Case investigation and contact tracing (CICT) is an important tool for communicable disease control, both to proactively interrupt chains of transmission and to collect information for situational awareness. We run the first randomized trial of COVID-19 CICT to investigate the utility of manual (i.e., call-based) vs. automated (i.e., survey-based) CICT for pandemic surveillance.Methods: Between December 15, 2021 and February 5, 2022, a stepped wedge cluster randomized trial was run in which Santa Clara County ZIP Codes progressively transitioned from manual to automated CICT. Eleven individual-level data fields on demographics and disease dynamics were observed for non-response. The data contains 106,522 positive cases across 29 ZIP Codes.Findings: Automated CICT reduced overall collected information by 29 percentage points (SE=0.08, p<0.01), as well as the response rate for individual fields. However, we find no evidence of differences in information loss by race or ethnicity.Interpretations: Automated CICT can serve as a useful alternative to manual CICT, with no substantial evidence of skewing data along racial or ethnic lines, but manual CICT improves completeness of key data for monitoring epidemiologic patterns.Funding: This research was supported in part by the Stanford Office of Community Engagement and the Stanford Institute for Human-Centered Artificial Intelligence.

    View details for DOI 10.1016/j.eclinm.2022.101726

    View details for PubMedID 36386031

  • Executive Control of Agency Adjudication: Capacity, Selection, and Precedential Rulemaking JOURNAL OF LAW ECONOMICS & ORGANIZATION Hausman, D. K., Ho, D. E., Krass, M. S., McDonough, A. 2022
  • Advances, challenges and opportunities in creating data for trustworthy AI NATURE MACHINE INTELLIGENCE Liang, W., Tadesse, G., Ho, D., Li, F., Zaharia, M., Zhang, C., Zou, J. 2022
  • Can transportation subsidies reduce failures to appear in criminal court? Evidence from a pilot randomized controlled trial ECONOMICS LETTERS Brough, R., Freedman, M., Ho, D. E., Phillips, D. C. 2022; 216
  • Science Translation During the COVID-19 Pandemic: An Academic-Public Health Partnership to Assess Capacity Limits in California. American journal of public health Maldonado, P., Peng, A., Ouyang, D., Suckale, J., Ho, D. E. 1800; 112 (2): 308-315


    On the basis of an extensive academic-public health partnership around COVID-19 response, we illustrate the challenge of science-policy translation by examining one of the most common nonpharmaceutical interventions: capacity limits. We study the implementation of a 20% capacity limit in retail facilities in the California Bay Area. Through a difference-in-differences analysis, we show that the intervention caused no material reduction in visits, using the same large-scale mobile device data on human movements (mobility data) originally used in the academic literature to support such limits. We show that the lack of effectiveness stems from a mismatch between the academic metric of capacity relative to peak visits and the policy metric of capacity relative to building code. The disconnect in metrics is amplified by mobility data losing predictive power after the early months of the pandemic, weakening the policy relevance of mobility-based interventions. Nonetheless, the data suggest that a better-grounded rationale for capacity limits is to reduce risk specifically during peak hours. To enhance the connection between science, policy, and public health in future times of crisis, we spell out 3 strategies: living models, coproduction, and shared metrics. (Am J Public Health. 2022;112(2):308-315.

    View details for DOI 10.2105/AJPH.2021.306576

    View details for PubMedID 35080959

  • Mapping Industrial Poultry Operations at Scale With Deep Learning and Aerial Imagery IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING Robinson, C., Chugg, B., Anderson, B., Ferres, J., Ho, D. E. 2022; 15: 7458-7471
  • Enhancing environmental enforcement with near real-time monitoring: Likelihood-based detection of structural expansion of intensive livestock farms INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION Chugg, B., Anderson, B., Eicher, S., Lee, S., Ho, D. E. 2021; 103
  • Disparate Limbo: How Administrative Law Erased Antidiscrimination YALE LAW JOURNAL Ceballos, C., Engstrom, D., Ho, D. E. 2021; 131 (2): 370-474
  • Disparate Limbo: How Administrative Law Erased Antidiscrimination YALE LAW JOURNAL Ceballos, C., Engstrom, D., Ho, D. E. 2021; 131 (2): 370-474
  • A language-matching model to improve equity and efficiency of COVID-19 contact tracing. Proceedings of the National Academy of Sciences of the United States of America Lu, L., Anderson, B., Ha, R., D'Agostino, A., Rudman, S. L., Ouyang, D., Ho, D. E. 2021; 118 (43)


    Contact tracing is a pillar of COVID-19 response, but language access and equity have posed major obstacles. COVID-19 has disproportionately affected minority communities with many non-English-speaking members. Language discordance can increase processing times and hamper the trust building necessary for effective contact tracing. We demonstrate how matching predicted patient language with contact tracer language can enhance contact tracing. First, we show how to use machine learning to combine information from sparse laboratory reports with richer census data to predict the language of an incoming case. Second, we embed this method in the highly demanding environment of actual contact tracing with high volumes of cases in Santa Clara County, CA. Third, we evaluate this language-matching intervention in a randomized controlled trial. We show that this low-touch intervention results in 1) significant time savings, shortening the time from opening of cases to completion of the initial interview by nearly 14 h and increasing same-day completion by 12%, and 2) improved engagement, reducing the refusal to interview by 4%. These findings have important implications for reducing social disparities in COVID-19; improving equity in healthcare access; and, more broadly, leveling language differences in public services.

    View details for DOI 10.1073/pnas.2109443118

    View details for PubMedID 34686604

  • Improving the Reliability of Food Safety Disclosure: Restaurant Grading in Seattle and King County, Washington JOURNAL OF ENVIRONMENTAL HEALTH Ashwood, Z. C., Elias, B., Ho, D. E. 2021; 84 (2): 30-37
  • Evaluation of Allocation Schemes of COVID-19 Testing Resources in a Community-Based Door-to-Door Testing Program JAMA HEALTH FORUM Chugg, B., Lu, L., Ouyang, D., Anderson, B., Ha, R., D'Agostino, A., Sujeer, A., Rudman, S. L., Garcia, A., Ho, D. E. 2021; 2 (8): e212260


    Overcoming social barriers to COVID-19 testing is an important issue, especially given the demographic disparities in case incidence rates and testing. Delivering culturally appropriate testing resources using data-driven approaches in partnership with community-based health workers is promising, but little data are available on the design and effect of such interventions.To assess and evaluate a door-to-door COVID-19 testing initiative that allocates visits by community health workers by selecting households in areas with a high number of index cases, by using uncertainty sampling for areas where the positivity rate may be highest, and by relying on local knowledge of the health workers.This cohort study was performed from December 18, 2020, to February 18, 2021. Community health workers visited households in neighborhoods in East San Jose, California, based on index cases or uncertainty sampling while retaining discretion to use local knowledge to administer tests. The health workers, also known as promotores de salud (hereinafter referred to as promotores) spent a mean of 4 days a week conducting door-to-door COVID-19 testing during the 2-month study period. All residents of East San Jose were eligible for COVID-19 testing. The promotores were selected from the META cooperative (Mujeres Empresarias Tomando Acción [Entrepreneurial Women Taking Action]).The promotores observed self-collection of anterior nasal swab samples for SARS-CoV-2 reverse transcriptase-polymerase chain reaction tests.A determination of whether door-to-door COVID-19 testing was associated with an increase in the overall number of tests conducted, the demographic distribution of the door-to-door tests vs local testing sites, and the difference in positivity rates among the 3 door-to-door allocation strategies.A total of 785 residents underwent door-to-door testing, and 756 were included in the analysis. Among the 756 individuals undergoing testing (61.1% female; 28.2% aged 45-64 years), door-to-door COVID-19 testing reached different populations than standard public health surveillance, with 87.6% (95% CI, 85.0%-89.8%) being Latinx individuals. The closest available testing site only reached 49.0% (95% CI, 48.3%-49.8%) Latinx individuals. Uncertainty sampling provided the most effective allocation, with a 10.8% (95% CI, 6.8%-16.0%) positivity rate, followed by 6.4% (95% CI, 4.1%-9.4%) for local knowledge, and 2.6% (95% CI, 0.7%-6.6%) for index area selection. The intervention was also associated with increased overall testing capacity by 60% to 90%, depending on the testing protocol.In this cohort study of 785 participants, uncertainty sampling, which has not been used conventionally in public health, showed promising results for allocating testing resources. Community-based door-to-door interventions and leveraging of community knowledge were associated with reduced demographic disparities in testing.

    View details for DOI 10.1001/jamahealthforum.2021.2260

    View details for Web of Science ID 000837103500002

    View details for PubMedID 35977196

    View details for PubMedCentralID PMC8796878

  • How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals. Nature medicine Wu, E., Wu, K., Daneshjou, R., Ouyang, D., Ho, D. E., Zou, J. 2021

    View details for DOI 10.1038/s41591-021-01312-x

    View details for PubMedID 33820998

  • Brevity, Speed, and Deference: An Account from the Williams Chambers YALE JOURNAL ON REGULATION Hausman, D. K., Ho, D. E., O'Connell, A. 2021; 38 (3): 745-751
  • Mandatory Retirement and Age, Race, and Gender Diversity of University Faculties AMERICAN LAW AND ECONOMICS REVIEW Ho, D. E., Mbonu, O., McDonough, A. 2021; 23 (1): 100-136
  • The Effectiveness of a Neighbor-to-Neighbor Get-Out-the-Vote Program: Evidence from the 2017 Virginia State Elections JOURNAL OF EXPERIMENTAL POLITICAL SCIENCE Handan-Nader, C., Ho, D. E., Morantz, A., Rutter, T. A. 2021; 8 (2): 145-160
  • How US law will evaluate artificial intelligence for covid-19. BMJ (Clinical research ed.) Krass, M. n., Henderson, P. n., Mello, M. M., Studdert, D. M., Ho, D. E. 2021; 372: n234

    View details for DOI 10.1136/bmj.n234

    View details for PubMedID 33722811

  • Algorithmic Accountability in the Administrative State YALE JOURNAL ON REGULATION Engstrom, D., Ho, D. E. 2020; 37 (3): 800–854
  • Improving Scientific Judgments in Law and Government: A Field Experiment of Patent Peer Review JOURNAL OF EMPIRICAL LEGAL STUDIES Ho, D. E., Ouellette, L. 2020; 17 (2): 190–223

    View details for DOI 10.1111/jels.12249

    View details for Web of Science ID 000534645700001

  • Due Process and Mass Adjudication: Crisis and Reform STANFORD LAW REVIEW Ames, D., Handan-Nader, C., Ho, D. E., Marcus, D. 2020; 72 (1): 1–78
  • Feasible Policy Evaluation by Design: A Randomized Synthetic Stepped-Wedge Trial of Mandated Disclosure in King County. Evaluation review Handan-Nader, C. n., Ho, D. E., Elias, B. n. 2020: 193841X20930852


    Evidence-based policy is limited by the perception that randomized controlled trials (RCTs) are expensive and infeasible. We argue that carefully tailored research design can overcome these challenges and enable more widespread randomized evaluations of policy implementation. We demonstrate how a stepped-wedge (randomized rollout) design that adapts synthetic control methods overcame substantial practical, administrative, political, and statistical constraints to evaluating King County's new food safety rating system. The core RCT component of the evaluation came at little financial cost to the government, allowed the entire county to be treated, and resulted in no functional implementation delay. The case of restaurant sanitation grading has played a critical role in the scholarship on information disclosure, and our study provides the first evidence from a randomized trial of the causal effects of grading on health outcomes. We find that the grading system had no appreciable effects on foodborne illness, hospitalization, or food handling practices but that the system may have marginally increased public engagement by encouraging higher reporting.

    View details for DOI 10.1177/0193841X20930852

    View details for PubMedID 32527152

  • Menu labeling, calories, and nutrient density: Evidence from chain restaurants. PloS one Ho, D. E., Mbonu, O. n., McDonough, A. n., Pottash, R. n. 2020; 15 (5): e0232656


    The Food and Drug Administration's menu labeling rule requires chain restaurants to prominently display calories, while leaving other nutritional information (e.g., fat, sodium, sugar) to the request of consumers. We use rich micronutrient data from 257 large chain brands and 24,076 menu items to examine whether calories are correlated with widely used "nutrient profile" scores that measure healthfulness based on nutrient density. We show that calories are indeed statistically significant predictors of nutrient density. However, as a substantive matter, the correlation is highly attenuated (partial R2 < 0.01). Our findings (a) suggest that the promise of calorie labeling to improve nutrient intake quality at restaurants is limited and (b) clarify the basis for transparency of nutrient composition beyond calories to promote healthy menu choices.

    View details for DOI 10.1371/journal.pone.0232656

    View details for PubMedID 32379786

  • New Evidence on Information Disclosure through Restaurant Hygiene Grading AMERICAN ECONOMIC JOURNAL-ECONOMIC POLICY Ho, D. E., Ashwood, Z. C., Handan-Nader, C. 2019; 11 (4): 404–28
  • Quality Review of Mass Adjudication: A Randomized Natural Experiment at the Board of Veterans Appeals, 2003-16 JOURNAL OF LAW ECONOMICS & ORGANIZATION Ho, D. E., Handan-Nader, C., Ames, D., Marcus, D. 2019; 35 (2): 239–88
  • Deep learning to map concentrated animal feeding operations NATURE SUSTAINABILITY Handan-Nader, C., Ho, D. E. 2019; 2 (4): 298–306
  • When Algorithms Import Private Bias into Public Enforcement: The Promise and Limitations of Statistical Debiasing Solutions Altenburger, K. M., Ho, D. E. J C B MOHR. 2019: 98–122
  • Is Yelp Actually Cleaning Up the Restaurant Industry? A Re-Analysis on the Relative Usefulness of Consumer Reviews Altenburger, K. M., Ho, D. E., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2019: 2543–50
  • Do Checklists Make a Difference? A Natural Experiment from Food Safety Enforcement JOURNAL OF EMPIRICAL LEGAL STUDIES Ho, D. E., Sherman, S., Wyman, P. 2018; 15 (2): 242–77
  • Does Peer Review Work? An Experiment of Experimentalism STANFORD LAW REVIEW Ho, D. E. 2017; 69 (1): 1-119
  • Managing Street-Level Arbitrariness: The Evidence Base for Public Sector Quality Improvement ANNUAL REVIEW OF LAW AND SOCIAL SCIENCE, VOL 13 Ho, D. E., Sherman, S., Hagan, J. 2017; 13: 251–72
  • Randomizing ... What? A Field Experiment of Child Access Voting Laws JOURNAL OF INSTITUTIONAL AND THEORETICAL ECONOMICS-ZEITSCHRIFT FUR DIE GESAMTE STAATSWISSENSCHAFT Ho, D. E. 2015; 171 (1): 150-170
  • Does Class Size Affect the Gender Gap? A Natural Experiment in Law JOURNAL OF LEGAL STUDIES Ho, D. E., Kelman, M. G. 2014; 43 (2): 291-321

    View details for DOI 10.1086/676953

    View details for Web of Science ID 000344362400003

  • Foreword: Conference Bias JOURNAL OF EMPIRICAL LEGAL STUDIES Ho, D. E. 2013; 10 (4): 603–11

    View details for DOI 10.1111/jels.12031

    View details for Web of Science ID 000325985200001

  • Fudging the Nudge: Information Disclosure and Restaurant Grading YALE LAW JOURNAL Ho, D. E. 2012; 122 (3): 574-688
  • MatchIt: Nonparametric Preprocessing for Parametric Causal Inference JOURNAL OF STATISTICAL SOFTWARE Ho, D. E., Imai, K., King, G., Stuart, E. A. 2011; 42 (8)
  • Credible Causal Inference for Empirical Legal Studies ANNUAL REVIEW OF LAW AND SOCIAL SCIENCE, VOL 7 Ho, D. E., Rubin, D. B. 2011; 7: 17-40
  • How Not to Lie with Judicial Votes: Misconceptions, Measurement, and Models CALIFORNIA LAW REVIEW Ho, D. E., Quinn, K. M. 2010; 98 (3): 813-876
  • Improving the Presentation and Interpretation of Online Ratings Data with Model-Based Figures AMERICAN STATISTICIAN Ho, D. E., Quinn, K. M. 2008; 62 (4): 279-288
  • Evaluating Course Evaluations: An Empirical Analysis of a Quasi-Experiment at the Stanford Law School, 2000-2007 JOURNAL OF LEGAL EDUCATION Ho, D. E., Shapiro, T. H. 2008; 58 (3): 388-412
  • Estimating causal effects of ballot order from a randomized natural experiment - The California alphabet lottery, 1978-2002 PUBLIC OPINION QUARTERLY Ho, D. E., Imai, K. 2008; 72 (2): 216-240

    View details for DOI 10.1093/poq/nfn018

    View details for Web of Science ID 000256523900003

  • Measuring Explicit Political Positions of Media QUARTERLY JOURNAL OF POLITICAL SCIENCE Ho, D. E., Quinn, K. M. 2008; 3 (4): 353-377
  • Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference POLITICAL ANALYSIS Ho, D. E., Imai, K., King, G., Stuart, E. A. 2007; 15 (3): 199-236

    View details for DOI 10.1093/pan/mpl013

    View details for Web of Science ID 000248544000002

  • Randomization inference with natural experiments: An analysis of ballot effects in the 2003 California recall election 63rd Annual Meeting of the Midwest-Political-Science-Association Ho, D. E., Imai, K. AMER STATISTICAL ASSOC. 2006: 888–900