Active Learning for Decision-Making from Imbalanced Observational Data

Iiris Sundin, Peter Schulam, Eero Siivola, Aki Vehtari, Suchi Saria, Samuel Kaski

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Abstract

Machine learning can help personalized decision support by learning models to predict individual treatment effects (ITE). 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^(y~∣x~,a). An example case is personalized medicine and the decision of which treatment to give to a patient. A common problem when learning these models from observational data is imbalance, that is, difference in treated/control covariate distributions, which is known to increase the upper bound of the expected ITE estimation error. We propose to assess the decision-making reliability by estimating the ITE model’s Type S error rate, which is the probability of the model inferring the sign of the treatment effect wrong. Furthermore, we use the estimated reliability as a criterion for active learning, in order to collect new (possibly expensive) observations, instead of making a forced choice based on unreliable predictions. We demonstrate the effectiveness of this decision-making aware active learning in two decision-making tasks: in simulated data with binary outcomes and in a medical dataset with synthetic and continuous treatment outcomes.
Original languageEnglish
Title of host publication36th International Conference on Machine Learning, ICML 2019
Pages10578-10587
Number of pages10
ISBN (Electronic)9781510886988
Publication statusPublished - 2019
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Machine Learning - Long Beach, United States
Duration: 9 Jun 201915 Jun 2019
Conference number: 36

Publication series

NameProceedings of Machine Learning Research
PublisherPMLR
Volume97
ISSN (Electronic)1938-7228

Conference

ConferenceInternational Conference on Machine Learning
Abbreviated titleICML
CountryUnited States
CityLong Beach
Period09/06/201915/06/2019

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  • Cite this

    Sundin, I., Schulam, P., Siivola, E., Vehtari, A., Saria, S., & Kaski, S. (2019). Active Learning for Decision-Making from Imbalanced Observational Data. In 36th International Conference on Machine Learning, ICML 2019 (pp. 10578-10587). (Proceedings of Machine Learning Research; Vol. 97).