15 citations · 25 across the 10 of their papers we have counts for
11 papers
Nonparametric Regression Discontinuity Designs with Survival Outcomes
Maximilian Schuessler, Erik Sverdrup, Robert Tibshirani +1
Quasi-experimental evaluations are central for generating real-world causal evidence and complementing insights from randomized trials. The regression discontinuity design (RDD) is…
balnet: Pathwise Estimation of Covariate Balancing Propensity Scores
Erik Sverdrup, Trevor Hastie
We present balnet, an R package for scalable pathwise estimation of covariate balancing propensity scores via logistic covariate balancing loss functions. Regularization paths are…
Efficient Log-Rank Updates for Random Survival Forests
Erik Sverdrup, James Yang, Michael LeBlanc
Random survival forests are widely used for estimating covariate-conditional survival functions under right-censoring. Their standard log-rank splitting criterion is typically reco…
Statistical Learning for Heterogeneous Treatment Effects: Pretraining, Prognosis, and Prediction
Maximilian Schuessler, Erik Sverdrup, Robert Tibshirani
Robust estimation of heterogeneous treatment effects is a fundamental challenge for optimal decision-making in domains ranging from personalized medicine to educational policy. In…
Estimating Treatment Effect Heterogeneity in Psychiatry: A Review and Tutorial with Causal Forests
Erik Sverdrup, Maria Petukhova, Stefan Wager
Flexible machine learning tools are increasingly used to estimate heterogeneous treatment effects. This paper gives an accessible tutorial demonstrating the use of the causal fores…
Treatment heterogeneity with right-censored outcomes using grf
Erik Sverdrup, Stefan Wager
This article walks through how to estimate conditional average treatment effects (CATEs) with right-censored time-to-event outcomes using the function causal_survival_forest (Cui e…
Qini Curves for Multi-Armed Treatment Rules
Erik Sverdrup, Han Wu, Susan Athey +1
Qini curves have emerged as an attractive and popular approach for evaluating the benefit of data-driven targeting rules for treatment allocation. We propose a generalization of th…
Proximal Causal Learning of Conditional Average Treatment Effects
Erik Sverdrup, Yifan Cui
Efficiently and flexibly estimating treatment effect heterogeneity is an important task in a wide variety of settings ranging from medicine to marketing, and there are a considerab…
Treatment Heterogeneity for Survival Outcomes
Yizhe Xu, Nikolaos Ignatiadis, Erik Sverdrup +3
Estimation of conditional average treatment effects (CATEs) plays an essential role in modern medicine by informing treatment decision-making at a patient level. Several metalearne…
What Makes Forest-Based Heterogeneous Treatment Effect Estimators Work?
Susanne Dandl, Torsten Hothorn, Heidi Seibold +3
Estimation of heterogeneous treatment effects (HTE) is of prime importance in many disciplines, ranging from personalized medicine to economics among many others. Random forests ha…
Estimating heterogeneous treatment effects with right-censored data via causal survival forests
Yifan Cui, Michael R. Kosorok, Erik Sverdrup +2
Forest-based methods have recently gained in popularity for non-parametric treatment effect estimation. Building on this line of work, we introduce causal survival forests, which c…