Active Learning for Decision-Making from Imbalanced Observational Data

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Researchers

Research units

  • Johns Hopkins University

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.

Details

Original languageEnglish
Title of host publicationProceedings of the 36th International Conference on Machine Learning
Publication statusPublished - 2019
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Machine Learning - Long Beach, United States
Duration: 10 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
Period10/06/201915/06/2019

ID: 34076603