Samuel E. DeWitt is a Data Scientist and Senior Researcher at the American Institutes for Research with over a decade of experience using advanced analytics and experimental methods to inform strategy, improve operations, and drive evidence-based decision-making. With deep expertise in causal inference, experimental design, and statistical modeling, Sam has led multi-million-dollar research efforts for public and nonprofit organizations, applying rigorous methodologies to address real-world challenges. He combines technical fluency in R, Python, Stata, and SQL with a strong track record of translating complex findings into actionable insights for stakeholders across sectors.
Previously a Professor at UNC Charlotte, Sam brings a unique blend of academic rigor and real-world application to his work. He has authored numerous peer-reviewed publications—many of which are publicly available on his Research Gate page—and presented his findings at national and regional conferences to inform evidence-based decision-making. In prior projects, he has applied machine learning techniques such as support vector machines, random forests, and neural networks to classification and prediction problems in the public sector. Sam also taught these methods to junior scholars, and his instructional materials are available here.
More recently, he is exploring synthetic control difference-in-differences models and longitudinal social network analysis to understand the impact of community-wide programs on public safety. He is passionate about making data science accessible, impactful, and policy-relevant—both within organizations and across the broader public landscape.
For those interested, I am also working to make more of my prior and upcoming work available on GitHub.