Zach Shahn

Zach Shahn is an Assistant Professor in the Department of Epidemiology and Biostatistics at the CUNY School of Public Health. He holds a PhD in Statistics from Columbia University and a BA in Mathematics from Stanford University. His research focuses on causal inference methods applied to healthcare data, particularly time-varying treatment effects and critical care applications. He has conducted postdoctoral work at Harvard School of Public Health and previously worked at IBM Research in Healthcare and Life Sciences.

Education:

• PhD in Statistics and Probability, Columbia University
• BA in Mathematics, Stanford University

Research interests include developing causal inference frameworks for evaluating treatment efficacy in dynamic healthcare settings, with a focus on critical care outcomes and methodological innovations. Recent work explores applications of causal diagrams, instrumental variables, and machine learning in healthcare decision-making. His studies often involve large-scale healthcare datasets to assess interventions' real-world impacts.

Key trends in his articles include advancements in causal effect estimation under complex treatment regimes, bias analysis in observational studies, and methodological contributions to difference-in-differences and N-of-1 trial designs. His work bridges statistical theory with practical healthcare challenges, emphasizing reproducibility and policy relevance.

No scientific awards are listed, though his contributions to causal inference methodologies are notable in academic circles. He has advised on healthcare data projects and published extensively without explicitly listed grants. His professional network includes collaborations with institutions like Harvard and IBM.

He is affiliated with CUNY's public health programs and actively engages in academic discourse via Twitter and LinkedIn. His lab or team details are not explicitly mentioned in available materials.