15 citations · 25 across the 10 of their papers we have counts for
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stat.CO2023★ 1 cited
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…
stat.ME2023
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…
stat.ML2023★ 2 cited
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…