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Found 12 papers, 2 papers with code
AI-Assisted Causal Pathway Diagram for Human-Centered Design
This paper explores the integration of causal pathway diagrams (CPD) into human-centered design (HCD), investigating how these diagrams can enhance the early stages of the design process.
Effect-Invariant Mechanisms for Policy Generalization
A major challenge in policy learning is how to adapt efficiently to unseen environments or tasks.
Assessing the Impact of Context Inference Error and Partial Observability on RL Methods for Just-In-Time Adaptive Interventions
Just-in-Time Adaptive Interventions (JITAIs) are a class of personalized health interventions developed within the behavioral science community.
Did we personalize? Assessing personalization by an online reinforcement learning algorithm using resampling
We use a working definition of personalization and introduce a resampling-based methodology for investigating whether the personalization exhibited by the RL algorithm is an artifact of the RL algorithm stochasticity.
Doubly robust nearest neighbors in factor models
We consider a matrix completion problem with missing data, where the $(i, t)$-th entry, when observed, is given by its mean $f(u_i, v_t)$ plus mean-zero noise for an unknown function $f$ and latent factors $u_i$ and $v_t$.
Counterfactual inference for sequential experiments
Our goal is to provide inference guarantees for the counterfactual mean at the smallest possible scale -- mean outcome under different treatments for each unit and each time -- with minimal assumptions on the adaptive treatment policy.
IntelligentPooling: Practical Thompson Sampling for mHealth
In this work we are concerned with the following challenges: 1) individuals who are in the same context can exhibit differential response to treatments 2) only a limited amount of data is available for learning on any one individual, and 3) non-stationary responses to treatment.
Batch Policy Learning in Average Reward Markov Decision Processes
The performance of the method is illustrated by simulation studies and an analysis of a mobile health study promoting physical activity.
Rapidly Personalizing Mobile Health Treatment Policies with Limited Data
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 individuals.
Off-Policy Estimation of Long-Term Average Outcomes with Applications to Mobile Health
The mHealth intervention policies, often called just-in-time adaptive interventions, are decision rules that map an individual's current state (e. g., individual's past behaviors as well as current observations of time, location, social activity, stress and urges to smoke) to a particular treatment at each of many time points.
Personalized HeartSteps: A Reinforcement Learning Algorithm for Optimizing Physical Activity
In this paper, we develop a Reinforcement Learning (RL) algorithm that continuously learns and improves the treatment policy embedded in the JITAI as the data is being collected from the user.
Personalizing Intervention Probabilities By Pooling
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.
