Post hoc subgroups in clinical trials: Anathema or analytics?

By H. Weisberg, Victor P Pontes. 2015

45 citations recorded by the source corpus.

Subfields: Statistics and Probability, Medical & Health AI

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistics and Probability

1. Susan Athey
2. Guido W. Imbens
3. Stefan Wager
4. P. Richard Hahn
5. Huan Liu
6. Jundong Li
7. Ruocheng Guo
8. Lu Cheng

In Medical & Health AI

1. Susan Athey
2. Huan Liu
3. Guido W. Imbens

Citations in the corpus

• A Survey of Learning Causality with Data: Problems and Methods (2018, background): “Other similar work on learning sparse models for ATE estimation includes [134, 140].”
• Machine Learning Methods for Estimating Heterogeneous Causal Eects (2015, methodology): “As discussed above, [28] and [35] apply an analog of the TOT approach to regression settings, but do not consider alternative approaches for goodness of fit measures.”
• A Survey of Learning Causality with Data (2018, background): “disjoint subsets of features Z u,V u ⊂X u, λ v and λ z are the hyperparameters that control the sparsity of the learned model. Other similar work on learning sparse models for ATE estimation includes [134,140]. Learning Heterogeneous Causal Effects with Ensembles.Ensemble models can provide comparab”