8 citations · 14 across the 7 of their papers we have counts for
5 papers · 1 filter
Multivariate Probabilistic Regression with Natural Gradient Boosting
Michael O'Malley, Adam M. Sykulski, Rick Lumpkin +1
Many single-target regression problems require estimates of uncertainty along with the point predictions. Probabilistic regression algorithms are well-suited for these tasks. Howev…
Performance metrics for intervention-triggering prediction models do not reflect an expected reduction in outcomes from using the model
Alejandro Schuler, Aashish Bhardwaj, Vincent Liu
Clinical researchers often select among and evaluate risk prediction models using standard machine learning metrics based on confusion matrices. However, if these models are used t…
A comparison of methods for model selection when estimating individual treatment effects
Alejandro Schuler, Michael Baiocchi, Robert Tibshirani +1
Practitioners in medicine, business, political science, and other fields are increasingly aware that decisions should be personalized to each patient, customer, or voter. A given t…
Synth-Validation: Selecting the Best Causal Inference Method for a Given Dataset
Alejandro Schuler, Ken Jung, Robert Tibshirani +2
Many decisions in healthcare, business, and other policy domains are made without the support of rigorous evidence due to the cost and complexity of performing randomized experimen…
Some methods for heterogeneous treatment effect estimation in high-dimensions
Scott Powers, Junyang Qian, Kenneth Jung +4
When devising a course of treatment for a patient, doctors often have little quantitative evidence on which to base their decisions, beyond their medical education and published cl…