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Estimating dynamic treatment regimes (DTRs) from retrospective observational data is challenging as some degree of unmeasured confounding is often expected.
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Confounding-robust policy evaluation in infinite-horizon reinforcement learning
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Munos, R. (2003) · 2003
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Optimal dynamic treatment regimes
Murphy, S. A. (2003) · 2003
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Heng, S. and Small, D. S. (2020) · 2004
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A structural approach to selection bias
Hernán, M. A., Hernández-Díaz, S., and Robins, J. M. (2004) · 2004
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Nonparametric estimation of average treatment effects under exogeneity: A review
Imbens, G. W. (2004) · 2004
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Probability measures on metric spaces
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Swanson, S. A., Hernán, M. A., Miller, M., Robins, J. M., and Richardson, T. S. (2018) · 2018
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High-dimensional probability: An introduction with applications in data science
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Bounded, efficient and multiply robust estimation of average treatment effects using instrumental variables
Wang, L. and Tchetgen Tchetgen, E. (2018) · 2018
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C-learning: A new classification framework to estimate optimal dynamic treatment regimes
Zhang, B. and Zhang, M. (2018) · 2018
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Interpretable dynamic treatment regimes
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Assessment of tree-based statistical learning to estimate optimal personalized treatment decision rules for traumatic finger amputations
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Differential effects of delivery hospital on mortality and morbidity in minority premature and low birth weight neonates
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Selecting and ranking individualized treatment rules with unmeasured confounding
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Machine intelligence for individualized decision making under a counterfactual world: A rejoinder
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