Semi-supervised learning and the question of true versus estimated propensity scores
Herren, A., & Hahn, P. R. (2020). Semi-supervised learning and the question of true versus estimated propensity scores. arXiv preprint arXiv:2009.06183.
CV
Education
- Ph.D in Statistics, Arizona State University, 2019-2023, GPA 4.0/4.0
- B.A. in Mathematics and Economics, Georgetown University, 2008-2012, cum laude
Work experience
- University of Texas at Austin
- Postdoctoral Fellow (Summer 2023-Present)
- Microsoft
- Data Scientist Intern (Summer 2021)
- Arize AI
- Data Science Intern (Summer 2020)
- Oliver Wyman
- Senior Analyst, Retail and Business Banking Consumer Insights Group (Winter 2018 - Summer 2019)
- Analyst, Retail and Business Banking Consumer Insights Group (Summer 2014 - Winter 2018)
- Cornerstone Research
- Analyst (Summer 2012 - Summer 2014)
Research
Statistical Aspects of SHAP: Functional ANOVA for Model Interpretation
Herren, A., & Hahn, P. R. (2022). Statistical Aspects of SHAP: Functional ANOVA for Model Interpretation. arXiv preprint arXiv:2208.09970.
Feature selection in stratification estimators of causal effects: lessons from potential outcomes, causal diagrams, and structural equations
Hahn, P. R. & Herren, A. (2022). Feature selection in stratification estimators of causal effects: lessons from potential outcomes, causal diagrams, and structural equations. arXiv preprint arXiv:2209.11400.
Talks
Feature selection for learning causal effects: a finite-sample, stratification estimator perspective
Washington University in St. Louis, St. Louis, Missouri, USA
Teaching
- MAT 270 (Calculus with Analytic Geometry I) Recitation, Fall 2020
- MAT 271 (Calculus with Analytic Geometry II) Recitation, Fall 2021
- MAT 271 (Calculus with Analytic Geometry II) Recitation, Spring 2022