Abstract
The contributions of the review paper on the technical challenges and applications of dynamic treatment regimes are briefly discussed and potential extensions to continuous action spaces and high dimensional problems are indicated.
Highlights
I would like to start out by congratulating the authors of the article on both a lucid and precise exposition of some of the key inferential challenges that arise in the prescription of optimal dynamic treatment regimes (DTR on) which have found growing interest across the clinical sciences and have intimate connections to other forms of adaptive decision making
This particular discussant has had the good fortune to hear his colleague, Professor Murphy, and members of her group, speak on several occasions on this area and this review paper is the perfect icing on that cake, providing a great degree of clarity about the subtle mathematical issues that arise naturally in very canonical versions of this problem: non-regularity of least squares estimates of the parameters associated with the optimal DTR and issues related to the associated asymptotic bias
The paper is formulated within the context of Q-learning, an indirect estimation method for optimal DTR that is attractive when model building can be aided by expert opinion
Summary
I would like to start out by congratulating the authors of the article on both a lucid and precise exposition of some of the key inferential challenges that arise in the prescription of optimal dynamic treatment regimes (DTR on) which have found growing interest across the clinical sciences and have intimate connections to other forms of adaptive decision making (in engineering/robotics contexts). Abstract: The contributions of the review paper on the technical challenges and applications of dynamic treatment regimes are briefly discussed and potential extensions to continuous action spaces and high dimensional problems are indicated.
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