Multi armed bandit (MAB) algorithms have been increasingly used to complement or
integrate with A/B tests and randomized clinical trials in e-commerce, healthcare, and …

Sequential data collection has emerged as a widely adopted technique for enhancing the
efficiency of data gathering processes. Despite its advantages, such data collection …

Off-policy evaluation (OPE) is the problem of estimating the value of a target policy using
historical data collected under a different logging policy. OPE methods typically assume …

In the standard data analysis framework, data is first collected (once for all), and then data
analysis is carried out. Moreover, the data-generating process is typically assumed to be …

There is a great desire to use adaptive sampling methods, such as reinforcement learning
(RL) and bandit algorithms, for the real-time personalization of interventions in digital …

How can we induce social media users to be discerning when sharing information during a
pandemic? An experiment on Facebook Messenger with users from Kenya (n= 7,498) and …

We consider after-study statistical inference for sequentially designed experiments wherein
multiple units are assigned treatments for multiple time points using treatment policies that …

Off-policy learning, referring to the procedure of policy optimization with access only to
logged feedback data, has shown importance in various important real-world applications …

We consider the estimation of treatment effects in settings when multiple treatments are
assigned over time and treatments can have a causal effect on future outcomes or the state …

Estimation and inference in statistics pose significant challenges when data are collected
adaptively. Even in linear models, the Ordinary Least Squares (OLS) estimator may fail to …