Causal inference for time series analysis: Problems, methods and evaluation
Time series data are a collection of chronological observations which are generated by
several domains such as medical and financial fields. Over the years, different tasks such as …
several domains such as medical and financial fields. Over the years, different tasks such as …
A review of off-policy evaluation in reinforcement learning
Reinforcement learning (RL) is one of the most vibrant research frontiers in machine
learning and has been recently applied to solve a number of challenging problems. In this …
learning and has been recently applied to solve a number of challenging problems. In this …
Personalized heartsteps: A reinforcement learning algorithm for optimizing physical activity
With the recent proliferation of mobile health technologies, health scientists are increasingly
interested in developing just-in-time adaptive interventions (JITAIs), typically delivered via …
interested in developing just-in-time adaptive interventions (JITAIs), typically delivered via …
Efficacy of contextually tailored suggestions for physical activity: a micro-randomized optimization trial of HeartSteps
Background HeartSteps is an mHealth intervention that encourages regular walking via
activity suggestions tailored to the individuals' current context. Purpose We conducted a …
activity suggestions tailored to the individuals' current context. Purpose We conducted a …
[HTML][HTML] To prompt or not to prompt? A microrandomized trial of time-varying push notifications to increase proximal engagement with a mobile health app
Background: Mobile health (mHealth) apps provide an opportunity for easy, just-in-time
access to health promotion and self-management support. However, poor user engagement …
access to health promotion and self-management support. However, poor user engagement …
Design and analysis of switchback experiments
Switchback experiments, where a firm sequentially exposes an experimental unit to random
treatments, are among the most prevalent designs used in the technology sector, with …
treatments, are among the most prevalent designs used in the technology sector, with …
Designing reinforcement learning algorithms for digital interventions: pre-implementation guidelines
Online reinforcement learning (RL) algorithms are increasingly used to personalize digital
interventions in the fields of mobile health and online education. Common challenges in …
interventions in the fields of mobile health and online education. Common challenges in …
Which components of a smartphone walking app help users to reach personalized step goals? Results from an optimization trial
JN Kramer, F Künzler, V Mishra… - Annals of Behavioral …, 2020 - academic.oup.com
Abstract Background The Assistant to Lift your Level of activitY (Ally) app is a smartphone
application that combines financial incentives with chatbot-guided interventions to …
application that combines financial incentives with chatbot-guided interventions to …
Panel experiments and dynamic causal effects: A finite population perspective
In panel experiments, we randomly assign units to different interventions, measuring their
outcomes, and repeating the procedure in several periods. Using the potential outcomes …
outcomes, and repeating the procedure in several periods. Using the potential outcomes …
Time series experiments and causal estimands: exact randomization tests and trading
I Bojinov, N Shephard - Journal of the American Statistical …, 2019 - Taylor & Francis
We define causal estimands for experiments on single time series, extending the potential
outcome framework to dealing with temporal data. Our approach allows the estimation of a …
outcome framework to dealing with temporal data. Our approach allows the estimation of a …