Software

Generalized Random Forests
Forest-based methods for causal inference

Multi-Armed Qini
Treatment rule evaluation with multiple costly interventions

Policy Tree
Interpretable policy learning via optimal decision trees

Preprints and Publications

Statistical Learning for Heterogeneous Treatment Effects: Pretraining, Prognosis, and Prediction
Maximilian Schuessler, Erik Sverdrup, Robert Tibshirani
Preprint
[arxiv]

Qini Curves for Multi-Armed Treatment Rules
Erik Sverdrup, Han Wu, Susan Athey, Stefan Wager
Journal of Computational and Graphical Statistics, forthcoming
[paper, arxiv, github]

Estimating Treatment Effect Heterogeneity in Psychiatry: A Review and Tutorial with Causal Forests
Erik Sverdrup, Maria Petukhova, Stefan Wager
International Journal of Methods in Psychiatric Research, 34(2), 2025
[paper, 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]

Scientific collaborations

A Prediction Model for Differential Resilience to the Effects of Combat-related Stressors in US Army Soldiers
Ronald C. Kessler, Robert M. Bossarte, et al.
International Journal of Methods in Psychiatric Research, 33(4), 2024
[paper]

Developing an Individualized Treatment Rule for Veterans with Major Depressive Disorder Using Electronic Health Records
Nur Hani Zainal, Robert M. Bossarte, et al.
Molecular Psychiatry,  29(8), 2024
[paper]

Proof-of-concept of a Data-driven Approach to Estimate the Associations of Comorbid Mental and Physical Disorders with Global Health-related Disability
Ymkje Anna de Vries, Jordi Alonso, et al.
International Journal of Methods in Psychiatric Research, 33(1), 2024
[paper]

Other publications

Treatment Heterogeneity with Right-Censored Outcomes Using grf
Erik Sverdrup, Stefan Wager
Lifetime Data Science Newsletter (LiDS), January 2024
[arxiv]

Previous work in finance

Constrained Currency Stochastic Discount Factors
Piotr Orlowski, Valeri Sokolovski, Erik Sverdrup
[ssrn]

Financial Intermediary Risk and the Cross-section of Hedge-fund Returns
Magnus Dahlquist, Simon Rottke, Valeri Sokolovski, Erik Sverdrup
[ssrn]