Erik Sverdrup
Postdoctoral scholar
Stanford University
I am a postdoc at Stanford GSB with Stefan Wager and Susan Athey. I work on data science tools and statistical software for estimating causal effects that leverage advances in machine learning, statistics, and computation; as well as interdisciplinary applications. Fall 2024, I'll join Monash University, Department of Econometrics & Business Statistics, as a Senior Lecturer (Assistant Professor).
Software
Generalized Random Forests
Causal inference with nonparametric methods
Multi-Armed Qini
Treatment rule evaluation with multiple costly interventions
Policy Tree
Interpretable policy learning via optimal decision trees
Research
Qini Curves for Multi-Armed Treatment Rules
Erik Sverdrup, Han Wu, Susan Athey, Stefan Wager
Preprint
[arxiv, github]
What Makes Forest-Based Heterogeneous Treatment Effect Estimators Work?
Susanne Dandl, Christian Haslinger, Torsten Hothorn, Heidi Seibold, Erik Sverdrup, Stefan Wager, Achim Zeileis
Annals of Applied Statistics, 18(1), 2024
[paper, arxiv]
Estimated Average Treatment Effect of Psychiatric Hospitalization in Patients With Suicidal Behaviors: A Precision Treatment Analysis
Eric L. Ross, Robert M. Bossarte, [...], Erik Sverdrup, Stefan Wager, Ronald C. Kessler
JAMA Psychiatry, 81(2), 2024
[paper]
Low-intensity Fires Mitigate the Risk of High-intensity Wildfires in California's Forests
Xiao Wu, Erik Sverdrup, Michael D. Mastrandrea, Michael W. Wara, Stefan Wager
Science Advances, 9(45), 2023
[paper, statistical appendix, github]
Proximal Causal Learning of Conditional Average Treatment Effects
Erik Sverdrup, Yifan Cui
International Conference on Machine Learning, 2023
[paper, arxiv]
Estimating Heterogeneous Treatment Effects with Right-Censored Data via Causal Survival Forests
Yifan Cui, Michael R. Kosorok, Erik Sverdrup, Stefan Wager, Ruoqing Zhu
Journal of the Royal Statistical Society: Series B, 85(2), 2023
[paper, arxiv, github]
Treatment Heterogeneity with Survival Outcomes
Yizhe Xu, Nikolaos Ignatiadis, Erik Sverdrup, Scott Fleming, Stefan Wager, Nigam Shah
Chapter in: Handbook of Matching and Weighting Adjustments for Causal Inference. Chapman & Hall/CRC Press, 2023
[book, arxiv, github]
Doubly Robust Treatment Effect Estimation with Missing Attributes
Imke Mayer, Erik Sverdrup, Tobias Gauss, Jean-Denis Moyer, Stefan Wager, Julie Josse
Annals of Applied Statistics, 14(3), 2020
[paper, arxiv]
policytree: Policy Learning via Doubly Robust Empirical Welfare Maximization over Trees
Erik Sverdrup, Ayush Kanodia, Zhengyuan Zhou, Susan Athey, Stefan Wager
Journal of Open Source Software, 5(50), 2020
[paper, github]
Previous work in finance
Benchmark Currency Stochastic Discount Factors
Piotr Orlowski, Valeri Sokolovski, Erik Sverdrup
[ssrn]
Hedge Funds and Prime Broker Risk
Magnus Dahlquist, Simon Rottke, Valeri Sokolovski, Erik Sverdrup
[ssrn]