Scholar
Kaitlyn J. Lee
Google Scholar ID: sD0Wr-EAAAAJ
PhD Student, University of California, Berkeley
Citations & Impact
Citations
7
H-index
2
i10-index
0
Publications
3
Co-authors
0
Publications
Resume (English only)
Academic Achievements
- - Publications:
- * Lee, K. J., Hubbard, A., Schuler, A. (2025). Bridging Binarization: Causal Inference with Dichotomized Continuous Exposures. arXiv:2405.07109. Journal of Causal Inference (accepted, Oct 2025).
- * Lee, K. J., Schuler, A. (2025). RieszBoost: Gradient Boosting for Riesz Regression. arXiv:2501.04871.
- * Gordon, E. R., Trager, M. H., Kwinta, B. D., Stonesifer, C. J., Lee, K. J., … Geskin, L. J. (2024). Maintenance therapy for CTCL: importance for prevention of disease progression. Leukemia & Lymphoma, 1–8. https://doi.org/10.1080/10428194.2024.2376164.
- - Awards:
- * Tom Ten Have Poster Award Honorable Mention, American Causal Inference Conference | 2025
- * Certificate of Distinction in Teaching, Harvard University | Fall 2020
- - Conference Presentations:
- * Lee, K. J., Schuler, A. (2024, May). RieszBoost: Gradient Boosting for Riesz Regression. Poster presentation. American Causal Inference Conference, Detroit, MI.
- * Lee, K. J., Hubbard, A., Schuler, A. (2024, April). Bridging Binarization: Causal Inference with Dichotomized Continuous Treatments. Oral presentation. European Causal Inference Meeting, Ghent, Belgium.
- * Lee, K. J., Schuler, A. (2024, February). RieszBoost: Gradient Boosting for Riesz Regression. Oral presentation. Center for the Application of Mathematics and Statistics to Economics and Center for the Theoretical Foundations of Learning, Inference, Information, Intelligence, Mathematics and Microeconomics at Berkeley Conference, Berkeley, CA.
- * Lee, K. J., Hubbard, A., Schuler, A. (2024, May). Bridging Binarization: Causal Inference with Dichotomized Continuous Treatments. Poster presentation. American Causal Inference Conference, Seattle, WA.
- * Lee, K. J., Hubbard, A., Schuler, A. (2024, June). Bridging Binarization: Causal Inference with Dichotomized Continuous Treatments. Poster presentation. Society of Epidemiological Research Conference, Austin, TX.
Research Experience
- - Genentech, Product Data Science Intern, Summer 2025 - Present
- - Center for Targeted Machine Learning, UC Berkeley, Graduate Student Researcher, Fall 2023 - Present
- - Cornerstone Research, Analyst, January 2017 - June 2022
- - Huybers Lab, Harvard University, Research Assistant, May 2020 - December 2020
Education
- - UC Berkeley, PhD in Biostatistics, August 2022 - May 2027 (expected), Advisor: Professor Alejandro Schuler
- - UC Berkeley, MA in Biostatistics, August 2022 - August 2024
- - Harvard College, AB in Physics with Statistics Secondary, August 2016 - May 2020
Background
- Her research interests span causal inference, machine learning, and scalable statistical methods, with a focus on developing practical approaches to answer real-world questions in health, social policy, and clinical studies. Her current work includes designing efficient machine learning algorithms for causal effect estimation, bridging rigorous theory with applied data analysis.
Co-authors