1 citations · 1 across the 3 of their papers we have counts for
5 papers
ScoreStop: Gradient-based early stopping using functional score tests
Oliver J. Hines, Christian L. Hines
Gradient boosted decision trees require a stopping rule to avoid overfitting. The standard rule monitors a validation loss and stops if the loss fails to improve for a fixed patien…
Learning density ratios in causal inference using Bregman-Riesz regression
Oliver J. Hines, Caleb H. Miles
The ratio of two probability density functions is a fundamental quantity that appears in many areas of statistics and machine learning, including causal inference, reinforcement le…
Targeted Learning on Variable Importance Measure for Heterogeneous Treatment Effect
Haodong Li, Alan E Hubbard, Oliver J Hines +3
Quantifying the heterogeneity of treatment effect is important for understanding how a commercial product or medical treatment affects different population subgroups. While much of…
Riesz representers for the rest of us
Nicholas T. Williams, Oliver J. Hines, Kara E. Rudolph
The application of semiparametric efficient estimators, particularly those that leverage machine learning, is rapidly expanding within epidemiology and causal inference. This liter…
Automatic debiasing of neural networks via moment-constrained learning
Christian L. Hines, Oliver J. Hines
Causal and nonparametric estimands in economics and biostatistics can often be viewed as the mean of a linear functional applied to an unknown outcome regression function. Naively…