11 citations · 25 across the 5 of their papers we have counts for
6 papers
Contextual Bandits in a Survey Experiment on Charitable Giving: Within-Experiment Outcomes versus Policy Learning
Susan Athey, Undral Byambadalai, Vitor Hadad +3
We design and implement an adaptive experiment (a ``contextual bandit'') to learn a targeted treatment assignment policy, where the goal is to use a participant's survey responses…
Off-Policy Evaluation via Adaptive Weighting with Data from Contextual Bandits
Ruohan Zhan, Vitor Hadad, David A. Hirshberg +1
It has become increasingly common for data to be collected adaptively, for example using contextual bandits. Historical data of this type can be used to evaluate other treatment as…
Adapting to Misspecification in Contextual Bandits with Offline Regression Oracles
Sanath Kumar Krishnamurthy, Vitor Hadad, Susan Athey
Computationally efficient contextual bandits are often based on estimating a predictive model of rewards given contexts and arms using past data. However, when the reward model is…
Tractable contextual bandits beyond realizability
Sanath Kumar Krishnamurthy, Vitor Hadad, Susan Athey
Tractable contextual bandit algorithms often rely on the realizability assumption - i.e., that the true expected reward model belongs to a known class, such as linear functions. In…
Confidence Intervals for Policy Evaluation in Adaptive Experiments
Vitor Hadad, David A. Hirshberg, Ruohan Zhan +2
Adaptive experiment designs can dramatically improve statistical efficiency in randomized trials, but they also complicate statistical inference. For example, it is now well known…
Sufficient Representations for Categorical Variables
Jonathan Johannemann, Vitor Hadad, Susan Athey +1
Many learning algorithms require categorical data to be transformed into real vectors before it can be used as input. Often, categorical variables are encoded as one-hot (or dummy)…