2 papers
stat.ML2026
Deconfounding Scores and Representation Learning for Causal Effect Estimation with Weak Overlap
Oscar Clivio, Alexander D'Amour, Alexander Franks +3
Overlap, also known as positivity, is a key condition for causal treatment effect estimation. Many popular estimators suffer from high variance and become brittle when features dif…
stat.ML2025
Is merging worth it? Securely evaluating the information gain for causal dataset acquisition
Jake Fawkes, Lucile Ter-Minassian, Desi Ivanova +2
Merging datasets across institutions is a lengthy and costly procedure, especially when it involves private information. Data hosts may therefore want to prospectively gauge which…