Harsh Parikh

Harsh Parikh is an Assistant Professor in the Department of Biostatistics at the Yale School of Public Health. He leads the Causal Evidence and Decisions Studio (CEADS), developing machine learning-aided causal inference approaches for high-stakes decision-making in complex environments. His research focuses on creating rigorous tools for estimating heterogeneous treatment effects, trustworthy methods that allow domain experts to validate causal assumptions, and domain-conscious approaches that bridge theory and practice. Harsh’s work addresses critical issues at the intersection of public health and social policy, including generalizing experimental evidence across populations, analyzing long-term economic impacts of health shocks, understanding environmental consequences of commodity price shifts, and optimizing treatment regimes in critical care. He holds a Ph.D. in Computer Science from Duke University and completed his postdoctoral fellowship at Johns Hopkins Bloomberg School of Public Health.