Optimal dynamic treatment regimes

By Susan A. Murphy. 2003

1,128 citations recorded by the source corpus.

Subfields: Statistics and Probability, Causal Inference & Discovery

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Citations in the corpus

Reinforcement Learning in Healthcare: A Survey (2019, methodology): “timize functionals of the outcome distribution other than the expected value [169], [170]. Schulte et al. [171] provided systematic empirical studies of Q-learning and Advantage-learning (A-learning) [172] methods and illustrated their performance using data from an MDD study. Other approaches inclu”
Deep Reinforcement Learning for Dynamic Treatment Regimes on Medical Registry Data (2017, background): “Methods to identify the optimal DTRs have recently received attentions in the statistical community [7], [8], [9], [10], [11], [12], [13], [14], and most of exsiting methods focused on data from randomized clinical trials.”
Doubly Robust Policy Evaluation and Optimization (2014, background): “Causal inference techniques have been studied to estimate the mean response of a given DTR (e.g., Robins, 1986; Murphy, van der Laan and Robins, 2001), and to optimize DTR (e.g., Murphy, 2003; Orellana, Rotnitzky and Robins, 2010).”
Estimating treatment effect heterogeneity in randomized program evaluation (2013, methodology): “…method can be applied to observational data [e.g., see Zhang et al. (2012) who develop a doubly robust estimator for optimal treatment regimes] and longitudinal data settings where the derivation of optimal dynamic treatment regimes is a frequent goal [e.g., Murphy (2003), Zhao et al. (2011)].”
Efficiently Breaking the Curse of Horizon in Off-Policy Evaluation with Double Reinforcement Learning (2019, background): “Reinforcement learning in settings such as healthcare (Murphy, 2003) and education (Mandel et al., 2014) is often limited to the oﬀ-policy setting due to the inability to simulate and the costliness of exploration.”
Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learning (2019, background): “OPE is a key problem in both reinforcement learning (RL) [7, 13–15, 17, 23, 32] and contextual bandits (CB) [5, 19, 28] and it finds applications as varied as healthcare [18] and education [16].”
Learning When-to-Treat Policies (2019, methodology): “In order to identify causal effects, we make the standard assumptions of sequential ignorability and overlap (Hernán, Brumback, and Robins, 2001; Murphy, 2003; Robins, 1986, 2004).”
Informing sequential clinical decision-making through reinforcement learning: an empirical study (2010, methodology): “This problem design is similar to the problem of estimating treatment policies in the medical sciences (Murphy 2003; Pineau et al. 2007).”
Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes (2019, background): “, 2000; Thomas and Brunskill, 2016) and it finds applications as varied as healthcare (Murphy, 2003) and education (Mandel et al.”
Batch Mode Reinforcement Learning based on the Synthesis of Artificial Trajectories (2012, background): “Optimal control problems arise in many real-life applications, such as engineering (Ried-miller 2005), medicine (Robins 1986; Murphy et al. 2001; Murphy 2003) or artiﬁcial intelligence (Sutton and Barto 1998).”
Reinforcement Learning Strategies for Clinical Trials in Non-small Cell Lung Cancer (2011, background): “The concept is an extension and melding of dynamic treatment regimes in counterfactual frameworks (Murphy, 2003;Robins, 2004) and sequential multiple assignment randomized trials (Murphy, 2005a) to accommodate an irreversible disease state with a possible continuum of treatment options.”
PERFORMANCE GUARANTEES FOR INDIVIDUALIZED TREATMENT RULES (2011, background): “There is an abundance of statistical literature in this area [17, 20, 21, 27, 29, 30, 34, 35].”
The International Journal of Biostatistics (2017, neutral): “Structural nested models for modeling and estimating an optimal dynamic treatment were proposed by Murphy (2003), Robins (2003), Robins (2005a), Robins (2005b).”
What-If Reasoning with Counterfactual Gaussian Processes (2017, background): “Alternatively, A-learning directly learns the relative differences between competing policies [Murphy, 2003].”
Interactive model building for Q-learning (2014, background): “This problem is shared by variants of Q-learning (e.g., A-learning, Murphy, 2003; Blatt et al., 2004; Robins, 2004; Schulte et al., 2012).”
Estimating Dynamic Treatment Regimes in Mobile Health Using V-learning (2016, methodology): “Dynamic treatment regimes have been well-studied in the statistical and biomedical literature (Murphy, 2003; Robins, 2004; Moodie et al., 2007; Kosorok and Moodie, 2015; Chakraborty and Moodie, 2013) and furthermore, statistical considerations in mHealth have been studied by, for example, Liao et…”
Estimating Individualized Treatment Rules Using Outcome Weighted Learning (2012, background): “Recently, this research area has been of great interest in long-term management of chronic disease (see, e.g., Murphy et al. 2001; Thall, Sung, and Estey 2002; Murphy 2003; Robins 2004; Moodie, Richardson, and Stephens 2007; Zhao et al. 2011).”
Causal Effect Models for Realistic Individualized Treatment and Intention to Treat Rules (2007, positive): “The causal models for realistic treatment rules presented in this paper thus provide an alternative method for modelling and estimation of optimal dynamic treatment regimes from among a user-supplied set, based on a generalization of structural nested models (Robins (1989),Robins (1998), Robins (199”