Susan A. Murphy

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University of Michigan ∙ Harvard University

Professor of Statistics, Radcliffe Alumnae Professor at the Radcliffe Institute, Harvard University and Professor of Computer Science at the Harvard John A. Paulson School of Engineering and Applied Sciences, MacArthur Fellow, Elected to the Institute of Medicine of the National Academy of Sciences 2014.

Featured Co-authors

Joelle Pineau
87 publications
Ambuj Tewari
52 publications
Predrag Klasnja
12 publications
Lucas Janson
11 publications
Santosh Kumar
7 publications
Peng Liao
7 publications
Inbal Nahum-Shani
6 publications
Tianchen Qian
6 publications
Walter Dempsey
5 publications
Sabina Tomkins
4 publications
Daniel Almirall
3 publications

The Micro-Randomized Trial for Developing Digital Interventions: Experimental Design Considerations

Just-in-time adaptive interventions (JITAIs) are time-varying adaptive i...
04/23/2020 ∙ by Ashley E. Walton, et al. ∙ 0 ∙ share

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The Micro-Randomized Trial for Developing Digital Interventions: Data Analysis Methods

Although there is much excitement surrounding the use of mobile and wear...
04/21/2020 ∙ by Tianchen Qian, et al. ∙ 0 ∙ share

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Streamlined Empirical Bayes Fitting of Linear Mixed Models in Mobile Health

To effect behavior change a successful algorithm must make high-quality ...
03/28/2020 ∙ by Marianne Menictas, et al. ∙ 0 ∙ share

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Inference for Batched Bandits

As bandit algorithms are increasingly utilized in scientific studies, th...
02/08/2020 ∙ by Kelly W. Zhang, et al. ∙ 14 ∙ share

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Estimating Time-Varying Causal Excursion Effect in Mobile Health with Binary Outcomes

Advances in wearables and digital technology now make it possible to del...
06/03/2019 ∙ by Tianchen Qian, et al. ∙ 0 ∙ share

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Linear mixed models under endogeneity: modeling sequential treatment effects with application to a mobile health study

Mobile health is a rapidly developing field in which behavioral treatmen...
02/28/2019 ∙ by Tianchen Qian, et al. ∙ 0 ∙ share

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Practical Considerations for Data Collection and Management in Mobile Health Micro-randomized Trials

There is a growing interest in leveraging the prevalence of mobile techn...
12/27/2018 ∙ by Nicholas J. Seewald, et al. ∙ 0 ∙ share

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The stratified micro-randomized trial design: sample size considerations for testing nested causal effects of time-varying treatments

Technological advancements in the field of mobile devices and wearable s...
11/09/2017 ∙ by Walter Dempsey, et al. ∙ 0 ∙ share

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An Actor-Critic Contextual Bandit Algorithm for Personalized Mobile Health Interventions

Increasing technological sophistication and widespread use of smartphone...
06/28/2017 ∙ by Huitian Lei, et al. ∙ 0 ∙ share

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Active Learning for Developing Personalized Treatment

The personalization of treatment via bio-markers and other risk categori...
02/14/2012 ∙ by Kun Deng, et al. ∙ 0 ∙ share

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