Active Learning for Decision-Making from Imbalanced Observational Data

10 Apr 2019Iiris SundinPeter SchulamEero SiivolaAki VehtariSuchi SariaSamuel Kaski

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 $\tilde{x}$ and predicted outcomes $\hat{p}(\tilde{y} \mid \tilde{x}, a)$... (read more)

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