Statistical Methods for Dynamic Treatment Regimes: Reinforcement Learning, Causal Inference, and Personalized Medicine

By Bibhas Chakraborty, Erica E. Moodie. 2013

306 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistics and Probability

Citations in the corpus

Reinforcement Learning in Healthcare: A Survey (2019, background): “roidism), cardiovascular diseases (e.g., heart attacks and hypertension), various mental illnesses (e.g., depression and schizophrenia), cancer, HIV infection, obesity, and other oral health problems [81]. Long-term treatment of these illnesses is often made up of a sequence of medical intervention ”
Learning When-to-Treat Policies (2019, methodology): “…on dynamic decision rules is beyond the scope of this article; instead, we refer the reader to recent review papers (Vansteelandt and Joffe 2014; Kosorok and Laber 2019; Clifton and Laber 2020; Levine et al. 2020), as well as textbooks by Chakraborty and Moodie (2013) and Tsiatis et al. (2019).”
Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes (2019, methodology): “…model M1 is commonly the one assumed in the causal inference literature in the context of marginal structural model estimation (Robins, 2000; Robins et al., 2000) and DTRs (Chakraborty and Moodie, 2013; Murphy et al., 2001; Zhang et al., 2013),3 in RL one often assumes that the MDP model M2 holds.”
Adaptive Interventions in Child and Adolescent Mental Health (2016, unknown): “In the statistical literature, they are commonly referred to as “dynamic treatment regimens” (Chakraborty & Moodie, 2013; Laber, Lizotte, Qian, Pelham, & Murphy, 2014; Lavori & Dawson, 2004; Murphy, 2003; Murphy & Almirall, 2009; Shortreed & Moodie, 2012), “treatment policies” (Lunceford, Davidian,…”
Estimating Dynamic Treatment Regimes in Mobile Health Using V-learning (2016, background): “…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 al. (2015) and…”
Robust Hybrid Learning for Estimating Personalized Dynamic Treatment Regimens (2016, methodology): “See Chakraborty and Moodie (2013) for a detailed review of the current literature.”
Interactive Q-learning for Quantiles (2017, methodology): “We compare TIQ-learning to the Q -learning analysis of Schulte et al. (2012) and binary Q -learning (Chakraborty and Moodie 2013).”
Interactive model building for Q-learning (2014, methodology): “…case that the data-generating model is unknown; one approach would be to adopt nonparametric models for the Q-functions (e.g., Zhao et al., 2011; Moodie et al., 2013), but some clinicians are wary of black-box approaches and it 125 can be difficult to glean scientific knowledge from these…”
STATISTICAL INFERENCE FOR THE MEAN OUTCOME UNDER A POSSIBLY NON-UNIQUE OPTIMAL TREATMENT STRATEGY (2016, background): “For a general overview of recent work on optimal (D)TRs, see Chakraborty and Moodie (2013). Suppose one wishes to know the impact of implementing an optimal TR in the population, that is, one wishes to know the optimal value.”
A Bayesian view of doubly robust causal inference (2016, background): “Inverse probability of treatment weighted outcome regression It has been recognized by various authors (e.g. Røysland, 2011; Chakraborty and Moodie, 2013) that inverse probability of treatment weighting can be motivated through a change of probability measures, or equivalently, importance sampling.”
Augmented outcome-weighted learning for estimating optimal dynamic treatment regimens (2018, methodology): “6 - 13 See also the works of Chakraborty and Moodie 14 and Kosorok and Moodie 15 for a detailed review of the current literature.”
A Theoretical Analysis of Deep Q-Learning (2019, methodology): “ Levine et al. (2017); Agarwal et al. (2019); Fujimoto et al. (2019). Moreover, Q-learning, and reinforcement learning methods in general, have been widely applied to dynamic treatment regimes (DTR) (Chakraborty, 2013; Laber et al., 2014; Tsiatis, 2019), where the goal is to ﬁnd sequential decision ”