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Dynamic treatment regime (DTR) plays a critical role in precision medicinewhen assigning patient-specific treatments at multiple stages and optimizing along term clinical outcome. However, most of existing work about DTRs have beenfocused on categorical treatment scenarios, instead of continuous treatmentoptions. Also, the performances of regular black-box machine learning methodsand regular tree learning methods are lack of interpretability and globaloptimality respectively. In this paper, we propose a non-greedy globaloptimization method for dose search, namely Global Optimal Dosage Tree-basedlearning method (GoDoTree), which combines a robust estimation of thecounterfactual outcome mean with an interpretable and non-greedy decision treefor estimating the global optimal dynamic dosage treatment regime in amultiple-stage setting. GoDoTree-Learning recursively estimates how thecounterfactual outcome mean depends on a continuous treatment dosage usingdoubly robust estimators at each stage, and optimizes the stage-specificdecision tree in a non-greedy way. We conduct simulation studies to evaluatethe finite sample performance of the proposed method and apply it to a realdata application for optimal warfarin dose finding.

Non-greedy Tree-based Learning for Estimating Global Optimal Dynamic Treatment Decision Rules with Continuous Treatment Dosage