Abstract
In this chapter, we consider semi-parametric approaches to finding the optimal dynamic treatment regime via modeling contrasts of conditional mean outcomes. In particular, we present G-estimation and regret-based methods including an iterative minimization method. We elucidate the connections between the different types of models assumed (e.g. blips, regrets, and Q-functions) as well as the estimation approaches themselves.
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- 1.
While the 0/1 coding of treatment is widely used in the causal inference literature, the − 1/1 coding is more common in Q-learning and SMART design literature, and hence we will adopt it in this chapter as in the rest of the book.
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Chakraborty, B., Moodie, E.E.M. (2013). Semi-parametric Estimation of Optimal DTRs by Modeling Contrasts of Conditional Mean Outcomes. In: Statistical Methods for Dynamic Treatment Regimes. Statistics for Biology and Health. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7428-9_4
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DOI: https://doi.org/10.1007/978-1-4614-7428-9_4
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