5 papers
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…
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…
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…