9 citations · 18 across the 13 of their papers we have counts for
6 papers · 1 filter
Leveraging semantic similarity for experimentation with AI-generated treatments
Lei Shi, David Arbour, Raghavendra Addanki +2
Large Language Models (LLMs) enable a new form of digital experimentation where treatments combine human and model-generated content in increasingly sophisticated ways. The main me…
Handling Missing Responses under Cluster Dependence with Applications to Language Model Evaluation
Zhenghao Zeng, David Arbour, Avi Feller +3
Human annotations play a crucial role in evaluating the performance of GenAI models. Two common challenges in practice, however, are missing annotations (the response variable of i…
Online Balanced Experimental Design
David Arbour, Drew Dimmery, Tung Mai +1
e consider the experimental design problem in an online environment, an important practical task for reducing the variance of estimates in randomized experiments which allows for g…
Efficient Balanced Treatment Assignments for Experimentation
David Arbour, Drew Dimmery, Anup Rao
In this work, we reframe the problem of balanced treatment assignment as optimization of a two-sample test between test and control units. Using this lens we provide an assignment…
General Identification of Dynamic Treatment Regimes Under Interference
Eli Sherman, David Arbour, Ilya Shpitser
In many applied fields, researchers are often interested in tailoring treatments to unit-level characteristics in order to optimize an outcome of interest. Methods for identifying…
Permutation Weighting
David Arbour, Drew Dimmery, Arjun Sondhi
In observational causal inference, in order to emulate a randomized experiment, weights are used to render treatments independent of observed covariates. This property is known as…