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A Smoothed Q-Learning Algorithm for Estimating Optimal Dynamic Treatment Regimes ∗
| Content Provider | Semantic Scholar |
|---|---|
| Author | Fan, Yanqin He, Ming Su, Liangjun Zhou, Xiao-Hua |
| Copyright Year | 2016 |
| Abstract | In this paper we propose a smoothed Q-learning algorithm for estimating optimal dynamic treatment regimes. In contrast to the Q-learning algorithm in which non-regular inference is involved, we show that under assumptions adopted in this paper, the proposed smoothed Q-learning estimator is asymptotically normally distributed even when the Q-learning estimator is not and its asymptotic variance can be consistently estimated. As a result, inference based on the smoothed Q-learning estimator is standard. We derive the optimal smoothing parameter and propose a data-driven method for estimating it. The finite sample properties of the smoothed Q-learning estimator are studied and compared with several existing estimators including the Q-learning estimator via an extensive simulation study. We illustrate the new method by analyzing data from the Clinical Antipsychotic Trials of Intervention EffectivenessAlzheimer’s Disease (CATIE-AD) study. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.colorado.edu/Economics/seminars/SeminarArchive/2015-16/Fan.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Entity Name Part Qualifier - adopted Estimated Inference Iodine I 124 PU-AD Learning Disorders Population Parameter Q-learning Sample Variance Simulation Smoothing (statistical technique) algorithm |
| Content Type | Text |
| Resource Type | Article |
