Variable Selection for Estimating Optimal Sequential Treatment Decisions Using Bayesian Networks
We propose a variable selection method for estimating decision rules of optimal sequential treatment assignments when the decision-relevant variables are unknown. Standard variable selection methods are insufficient in this setting since they choose covariates that are predictive of the outcome, not those that interact with the treatment on the outcome and are therefore relevant for decision-makin
