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

By Bibhas Chakraborty. 2013

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Citations in the corpus

Learning Optimal Fair Policies (2018, methodology): “In healthcare, the problem of learning optimal policies is studied under the heading of dynamic treatment regimes (Chakraborty and Moodie 2013).”
TREE-BASED REINFORCEMENT LEARNING FOR ESTIMATING OPTIMAL DYNAMIC TREATMENT REGIMES (2018, background): “particular, the problem of developing optimal multi-stage decisions has strong resemblance to reinforcement learning (RL) (Chakraborty and Moodie, 2013).”
Interactive Q-learning for Quantiles (2017, methodology): “As pr(Y > λ) = E(1Y>λ), many approaches exist for estimating an optimal regime that maximizes the value function of the binary outcome 1Y>λ; one example is discrete Q-learning (Chakraborty and Moodie 2013).”
Dynamic treatment regimes: technical challenges and applications (2014, methodology): “Other indirect methods include Q-learning (Murphy, 2005b; Chakraborty and Moodie, 2013; Qian et al., 2013; Chakraborty and Murphy, 2014; Laber et al., 2014), A-learning (Murphy, 2003; Robins, 2004), and regret-regression (Henderson et al., 2009).”
Q-learning Residual Analysis: Application to The Effectiveness of Sequences of Antipsychotic Medications for Patients with Schizophrenia (2016, unknown): “The issue of model checking for Q-learning has been discussed in [17] where it was shown that the residuals of the standard Q-learning may not be used to detect the omission of important variables that interact with the treatment indicator in predicting the outcome.”