Evan D. Peet

Evan D. Peet
Codirector, RAND Center for Causal Inference; Economist; Professor, Pardee RAND Graduate School
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Ph.D. in economics, Duke University; M.A. in economics, Duke University; B.A. in economics, math, statistics, Brigham Young University

Media Resources

This researcher is available for interviews.

To arrange an interview, contact the RAND Office of Media Relations at (310) 451-6913, or email media@rand.org.

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Evan D. Peet (he/him/his) is an economist at the RAND Corporation, a professor of policy analysis at the Pardee RAND Graduate School, and codirector of the RAND Center for Causal Inference. His research focuses on health and health policy. Currently, Peet is focusing on substance use disorders and maternal and child health, seeking to understand the impacts and unintended consequences of policies, as well as the optimal design, targeting, and implementation of policies. Within substance abuse, he is leading studies of prescriber substitutionary responses to state opioid policies, and the indirect/downstream consequences of the opioid crisis. Peet is an expert in causal inference, econometrics, machine learning, and predictive modeling. As an example, he is currently leading efforts to integrate causal inference and predictive methods within healthcare and social service provider systems to alert providers to maternal/child risks and to individualized intervention recommendations. Additionally, in past studies Peet has examined: human capital impacts of pollution, health service utilization, social service utilization, child development, educational reforms, long-term labor market outcomes, equitable responses to COVID-19, and more. Prior to joining RAND, Peet received his Ph.D. in economics from Duke University and completed a postdoctoral fellowship in global and environmental health at Harvard University’s T.H. Chan School of Public Health.

Recent Projects

  • Prediction and Reduction of Infant Mortality
  • The Effects of Opioid Supply-Side Policies on Substitutionary Prescribing and Health
  • The Impacts of State Quality Care Collaboratives on Maternal Health Disparities by Race/Ethnicity
  • Improving Naloxone Access and Its Effects on Drug Abuse and Overdoses

Selected Publications

Peet, Evan D., Dana Schultz, and Susan L. Lovejoy, Using an Innovative Database and Machine Learning to Predict and Reduce Infant Mortality, RAND Corporation (RB-A858-1), 2021

Evan D. Peet, David Powell, Rosalie Liccardo Pacula, "Trends in Out-of-Pocket Costs for Naloxone by Drug Brand and Payer in the US, 2010-2018," JAMA Health Forum, 2022

Peet, Evan D., Brian G. Vegetabile, Matthew Cefalu, Joseph D. Pane, and Cheryl L. Damberg, Machine Learning in Public Policy: The Perils and the Promise of Interpretability, RAND Corporation (PE-A828-1), 2022

Evan D. Peet, Beth Dana, Flora Yaou Sheng, David Powell, Kanaka Shetty, Bradley D. Stein, "Trends in the Concurrent Prescription of Opioids and Gabapentin in the US, 2006 to 2018," JAMA Internal Medicine, 2022

Evan D. Peet, "Early-life Environment and Human Capital: Evidence from the Philippines," Environment and Development Economics, 26(1), 2020

Evan Peet, Gunther Fink, and Wafaie Fawzi, "Returns to Education in Developing Countries: Evidence from the Living Standards and Measurement Study Surveys," Economics of Education Review

Dana Schultz, Susan Lovejoy, Evan Peet, "Tackling Persistent and Large Disparities in Birth Outcomes in Allegheny County, Pennsylvania," Maternal and Child Health Journal, 2022

Lilian G. Perez, Evan D. Peet, Brian Vegetabile, Regina A. Shih, "Big Data Needs and Challenges to Advance Research on Racial and Ethnic Inequities in Maternal and Child Health," Women's Health Issues, 2021

Recent Media Appearances

Interviews: PainRelief.com


  • Pittsburgh

    The City of Pittsburgh's a Movie Star

    Pennsylvania lawmakers and other stakeholders should consider updates to the film tax credit law and economic development strategy to improve the state's competitiveness, enhance the stability of the industry, and fill gaps in the current workforce.

    Oct 5, 2023

    Pittsburgh Post-Gazette

  • Data Analysis

    Equitable Data Analysis: Lessons from a COVID-19 Research Collaborative

    The health inequities exposed by COVID-19 underscored the importance of collecting race-stratified data to inform local policymakers. For the public health researchers trying to provide that, the pandemic also revealed some major pitfalls, especially about relying on open-source data.

    Jul 27, 2021

    The RAND Blog

  • Global Climate Change

    Adapting to a Hotter World

    Because climate change is largely irreversible, mitigation alone won't solve the problem. While mitigation will prevent even greater, future climatic changes, adaptation — efforts to adjust to climate change's effects — will prepare the world for a new set of living conditions, whatever they may be.

    Oct 2, 2015

    U.S. News & World Report