On Data-Driven Prescriptive Analytics with Side Information: A Regularized Nadaraya-Watson Approach

Understand

We consider generic stochastic optimization problems in the presence of side information which enables a more insightful decision.

• The side information constitutes observable exogenous covariates that alter the conditional probability distribution of the random problem parameters.
• A decision maker who adapts her decisions according to the observed side information solves an optimization problem where the objective function is specified by the conditional expectation of the random cost.
• If the joint probability distribution is unknown, then the conditional expectation can be approximated in a data-driven manner using the Nadaraya-Watson (NW) kernel regression.