We consider the optimization of an uncertain objective over continuous and multi-dimensional decision spaces in problems in which we are only provided with observational data. We propose a novel algorithmic framework that is tractable, asymptotically consistent, and superior to comparable methods on example problems. Our approach leverages predictive machine learning methods and incorporates information on the uncertainty of the predicted outcomes for the purpose of prescribing decisions. We demonstrate the efficacy of our method on examples involving both synthetic and real data sets.

We consider the optimization of an uncertain objective over continuous and multidimensional decision spaces in problems in which we are only provided with observational data.

We consider the optimization of an uncertain objective over continuous and multidimensional decision spaces in problems in which we are only provided with observational data.

The paper proposes an algorithm that can learn good decision-making policies over a continuous set of decisions using only access to observational data. The problem is well-motivated, but the paper can be substantially strengthened. Quality: Ok The paper clearly motivates a weakness of direct regression (e.g. from context and decision, to predict expected cost). The regression models may have different uncertainty for different decisions, and so it is useful to include an empirical estimate of variance and bias of the regression when selecting decisions. The paper will be more informative by highlighting several choices of regression models, each with different V (variance) and B (bias), and observing how lambda_1 lambda_2 are tuned to pick low-cost decisions with high probability.

We consider the optimization of an uncertain objective over continuous and multi-dimensional decision spaces in problems in which we are only provided with observational data. We propose a novel algorithmic framework that is tractable, asymptotically consistent, and superior to comparable methods on example problems. Our approach leverages predictive machine learning methods and incorporates information on the uncertainty of the predicted outcomes for the purpose of prescribing decisions. We demonstrate the efficacy of our method on examples involving both synthetic and real data sets. Papers published at the Neural Information Processing Systems Conference.

We consider the optimization of an uncertain objective over continuous and multi-dimensional decision spaces in problems in which we are only provided with observational data. We propose a novel algorithmic framework that is tractable, asymptotically consistent, and superior to comparable methods on example problems. Our approach leverages predictive machine learning methods and incorporates information on the uncertainty of the predicted outcomes for the purpose of prescribing decisions. We demonstrate the efficacy of our method on examples involving both synthetic and real data sets.