Search Results for author: Susan A. Murphy

Found 10 papers, 2 papers with code

Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-implementation Guidelines

1 code implementation8 Jun 2022 Anna L. Trella, Kelly W. Zhang, Inbal Nahum-Shani, Vivek Shetty, Finale Doshi-Velez, Susan A. Murphy

Online reinforcement learning (RL) algorithms are increasingly used to personalize digital interventions in the fields of mobile health and online education.

reinforcement-learning reinforcement Learning

Estimating causal effects with optimization-based methods: A review and empirical comparison

no code implementations28 Feb 2022 Martin Cousineau, Vedat Verter, Susan A. Murphy, Joelle Pineau

In the absence of randomized controlled and natural experiments, it is necessary to balance the distributions of (observable) covariates of the treated and control groups in order to obtain an unbiased estimate of a causal effect of interest; otherwise, a different effect size may be estimated, and incorrect recommendations may be given.

Causal Inference

Comparison and Unification of Three Regularization Methods in Batch Reinforcement Learning

no code implementations16 Sep 2021 Sarah Rathnam, Susan A. Murphy, Finale Doshi-Velez

In batch reinforcement learning, there can be poorly explored state-action pairs resulting in poorly learned, inaccurate models and poorly performing associated policies.

reinforcement-learning reinforcement Learning

Statistical Inference with M-Estimators on Adaptively Collected Data

no code implementations NeurIPS 2021 Kelly W. Zhang, Lucas Janson, Susan A. Murphy

Yet there is a lack of general methods for conducting statistical inference using more complex models on data collected with (contextual) bandit algorithms; for example, current methods cannot be used for valid inference on parameters in a logistic regression model for a binary reward.

Decision Making Multi-Armed Bandits

Streamlined Empirical Bayes Fitting of Linear Mixed Models in Mobile Health

no code implementations28 Mar 2020 Marianne Menictas, Sabina Tomkins, Susan A. Murphy

For example, a mobile health (mHealth) application designed to increase physical activity must make contextually relevant suggestions to motivate users.

Inference for Batched Bandits

no code implementations NeurIPS 2020 Kelly W. Zhang, Lucas Janson, Susan A. Murphy

As bandit algorithms are increasingly utilized in scientific studies and industrial applications, there is an associated increasing need for reliable inference methods based on the resulting adaptively-collected data.

Multi-Armed Bandits

iSurvive: An Interpretable, Event-time Prediction Model for mHealth

no code implementations ICML 2017 Walter H. Dempsey, Alexander Moreno, Christy K. Scott, Michael L. Dennis, David H. Gustafson, Susan A. Murphy, James M. Rehg

We present a parameter learning method for GLM emissions and survival model fitting, and present promising results on both synthetic data and an mHealth drug use dataset.

Survival Analysis

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

no code implementations28 Jun 2017 Huitian Lei, Yangyi Lu, Ambuj Tewari, Susan A. Murphy

Increasing technological sophistication and widespread use of smartphones and wearable devices provide opportunities for innovative and highly personalized health interventions.

Statistical Inference in Dynamic Treatment Regimes

no code implementations30 Jun 2010 Eric B. Laber, Min Qian, Dan J. Lizotte, William E. Pelham, Susan A. Murphy

We then review an interesting challenge, that of nonregularity that often arises in this area.

Decision Making

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