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.
|Title of host publication||36th International Conference on Machine Learning, ICML 2019|
|Number of pages||10|
|Publication status||Published - 2019|
|MoE publication type||A4 Article in a conference publication|
|Event||International Conference on Machine Learning - Long Beach, United States|
Duration: 9 Jun 2019 → 15 Jun 2019
Conference number: 36
|Name||Proceedings of Machine Learning Research|
|Conference||International Conference on Machine Learning|
|Period||09/06/2019 → 15/06/2019|
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).