Dave Graham-Squire, PhD is a statistician working with multiple research groups in Family Health Care Nursing, the Division of Health and Society, and the Division of HIV, Infectious Diseases, and Global Medicine at San Francisco General Hospital. His research focuses on causal effects, particularly in observational studies and non-experimental settings, where randomized controlled studies are not possible. His methodological expertise includes statistical computation, large administrative dataset analysis, survey design, and machine learning techniques for prediction and classification. He has primarily applied these techniques to issues of public policy and medical research and has dedicated his career to improving the health of systematically marginalized populations such as persons experiencing homelessness, low-wage workers, undocumented children, and uninsured individuals and families. His work focuses on the intersection of health, income inequality, homelessness and policy, and in opportunities to draw meaningful conclusions about which policy interventions are effective at addressing these pressing issues.

His current roles include: biostatistician of an NIH-funded randomized controlled trial on community driven interventions to improve the uptake of the RSV vaccine in older Latino adults; lead analyst and data pipeline architect of a large international longitudinal study of family health outcomes; consulting statistician on public sector integrated health-social serve-housing database to understand how to best serve, and house, people experiencing homelessness.

Prior to his current position, Dr. Graham-Squire led a team of quantitative analysts as the Director of Statistics and Data Science at the UCSF Benioff Housing and Homeless Initiative (2020-2024). In that role he led the survey design of the California Statewide Survey of People Experiencing Homelessness (CASPEH), a groundbreaking representative sample of people experiencing homelessness, in the service of reducing homelessness and better serving unhoused individuals.

Before UCSF, Dr. Graham-Squire was the Lead Statistician and programmer at the UC Berkeley Center for Labor Research and Education (2006-2020), where he developed influential policy analysis tools. As sole programmer and architect, he built the initial version of the California Simulation of Insurance Markets (CalSIM), an economic microsimulation model that has generated dozens of policy briefs and become a key planning tool for Covered California. His 2014 CalSIM analysis, "A little investment goes a long way...", demonstrated how modest program changes could provide coverage to hundreds of thousands of undocumented Californians, contributing to the passage of Senate Bill 1005, the Health for All Act. He also developed a novel method combining administrative data with national survey data to estimate public program costs for specific worker groups. This methodology generated a series of reports documenting how public assistance programs effectively subsidize low-wage employers—work that proved influential in campaigns to raise the minimum wage to $15/hour across numerous states, counties, and cities, and was cited by New York Governor Andrew Cuomo when signing that state's $15 minimum wage law.

At UCSF, Dr. Graham-Squire has served on the mentorship committees of graduate student researchers, post-docs, and assistant professors, and supervised and mentored early career analysts. He received a doctorate in Statistics from the University of California at Berkeley in 2018.