@inproceedings{b0adade0b8f84ab58e81e73224595c89,
title = "Active learning for decision-making from imbalanced observational data",
abstract = "Machine learning can help personalized decision support by learning models to predict individual treatment effects (TTE). This work studies the reliability of prediction-based decision-making in a task of deciding which action a to take for a target unit after observing its covariates x and predicted outcomes p(ỹ",
author = "Iiris Sundin and Peter Schulam and Eero Siivola and Aki Vehtari and Suchi Saria and Samuel Kaski",
year = "2019",
month = jan,
day = "1",
language = "English (US)",
series = "36th International Conference on Machine Learning, ICML 2019",
publisher = "International Machine Learning Society (IMLS)",
pages = "10578--10587",
booktitle = "36th International Conference on Machine Learning, ICML 2019",
note = "36th International Conference on Machine Learning, ICML 2019 ; Conference date: 09-06-2019 Through 15-06-2019",
}