Assistant Professor, Yale University
2 papers at NeurIPS 2025
We develop a framework for partial identification of causal effects under violations of key assumptions when combining heterogeneous data sources.
If and when integrating studies with different outcome measures improves efficiency, showing that gains hinge on strong linking assumptions but carry bias risks, while weaker ones may offer finite-sample benefits.
