8 citations · 17 across the 8 of their papers we have counts for
9 papers
The Disparate Impacts of College Admissions Policies on Asian American Applicants
Joshua Grossman, Sabina Tomkins, Lindsay Page +1
There is debate over whether Asian American students are admitted to selective colleges and universities at lower rates than white students with similar academic qualifications. Ho…
Doubly robust nearest neighbors in factor models
Raaz Dwivedi, Caleb Chin, Sabina Tomkins +3
We introduce and analyze an improved variant of nearest neighbors (NN) for estimation with missing data in latent factor models. We consider a matrix completion problem with missin…
Counterfactual inference in sequential experiments
Raaz Dwivedi, Katherine Tian, Sabina Tomkins +3
We consider after-study statistical inference for sequentially designed experiments wherein multiple units are assigned treatments for multiple time points using treatment policies…
Blocks as geographic discontinuities: The effect of polling place assignment on voting
Sabina Tomkins, Keniel Yao, Johann Gaebler +4
A potential voter must incur a number of costs in order to successfully cast an in-person ballot, including the costs associated with identifying and traveling to a polling place.…
Fast Physical Activity Suggestions: Efficient Hyperparameter Learning in Mobile Health
Marianne Menictas, Sabina Tomkins, Susan Murphy
Users can be supported to adopt healthy behaviors, such as regular physical activity, via relevant and timely suggestions on their mobile devices. Recently, reinforcement learning…
IntelligentPooling: Practical Thompson Sampling for mHealth
Sabina Tomkins, Peng Liao, Predrag Klasnja +1
In mobile health (mHealth) smart devices deliver behavioral treatments repeatedly over time to a user with the goal of helping the user adopt and maintain healthy behaviors. Reinfo…
Streamlined Empirical Bayes Fitting of Linear Mixed Models in Mobile Health
Marianne Menictas, Sabina Tomkins, Susan A Murphy
To effect behavior change a successful algorithm must make high-quality decisions in real-time. For example, a mobile health (mHealth) application designed to increase physical act…
Rapidly Personalizing Mobile Health Treatment Policies with Limited Data
Sabina Tomkins, Peng Liao, Predrag Klasnja +2
In mobile health (mHealth), reinforcement learning algorithms that adapt to one's context without learning personalized policies might fail to distinguish between the needs of indi…
Personalizing Intervention Probabilities By Pooling
Sabina Tomkins, Predrag Klasnja, Susan Murphy
In many mobile health interventions, treatments should only be delivered in a particular context, for example when a user is currently stressed, walking or sedentary. Even in an op…